About the Role

Together AI is building high-performance inference compute and the software platform around it. We’re looking for an early-career Data Warehouse Engineer with strong fundamentals and high growth potential to grow into a technical lead over time. You’ll contribute to designing and operating our data warehouse, ETL pipelines and orchestration, work on core data models and metrics, and help raise the bar on data quality and governance across the org — with mentorship and support from experienced engineers.

Requirements

• 0–4 years of professional experience (or strong internships/projects) working with data warehouses, pipelines, or analytics engineering.
• Solid SQL fundamentals — you’re comfortable writing queries and have some exposure to window functions or dimensional modeling concepts.
• Some hands-on experience with dbt or Airflow, or strong eagerness to learn — coursework and personal projects count.
• Basic Python for scripting and data tooling; any exposure to Spark (PySpark/SQL) is a plus.
• Familiarity with data modeling concepts like SCD2 or star schemas — even if only from coursework.
• Good communication skills: you can ask clarifying questions, explain your reasoning, and work with stakeholders to understand their needs.
• High standards for data quality, reliability, and maintainability — you care about getting things right.

Responsibilities

• Contribute to building and maintaining a medallion/curated data warehouse stack (bronze/silver/gold) for product, usage, billing, and operational data.
• Build and maintain Airflow orchestrated pipelines and dbt transformation projects (modular, tested, documented).
• Help design analytics-ready models: SCD Type 2, star schemas, and appropriate normalization for upstream canonical layers.
• Learn and apply Master Data Management (MDM) patterns (golden records, reference data, deduping, identity resolution).
• Implement data quality checks (freshness, nulls, referential integrity, distribution drift, anomaly detection).
• Contribute to data governance habits: data stewardship, ownership, SLAs, and clear definitions for “source of truth.”
• Help build and maintain a business semantic layer (consistent metric definitions, dimensions, and reusable logic) used by notebooks/BI.
• Partner with stakeholders (Product, Engineering, Finance, GTM, Ops) to translate questions into durable datasets and metrics.
• Use SQL, Python, and Spark where scale demands it; optimize for correctness, performance, and cost.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $130,000 - $170,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

Please see our privacy policy at https://www.together.ai/privacy  

About the Role

Together AI is building high-performance inference compute and the software platform around it. We’re looking for an early-career Data Warehouse Engineer with strong fundamentals and high growth potential to grow into a technical lead over time. You’ll contribute to designing and operating our data warehouse, ETL pipelines and orchestration, work on core data models and metrics, and help raise the bar on data quality and governance across the org — with mentorship and support from experienced engineers.

Requirements

• 0–4 years of professional experience (or strong internships/projects) working with data warehouses, pipelines, or analytics engineering.
• Solid SQL fundamentals — you’re comfortable writing queries and have some exposure to window functions or dimensional modeling concepts.
• Some hands-on experience with dbt or Airflow, or strong eagerness to learn — coursework and personal projects count.
• Basic Python for scripting and data tooling; any exposure to Spark (PySpark/SQL) is a plus.
• Familiarity with data modeling concepts like SCD2 or star schemas — even if only from coursework.
• Good communication skills: you can ask clarifying questions, explain your reasoning, and work with stakeholders to understand their needs.
• High standards for data quality, reliability, and maintainability — you care about getting things right.

Responsibilities

• Contribute to building and maintaining a medallion/curated data warehouse stack (bronze/silver/gold) for product, usage, billing, and operational data.
• Build and maintain Airflow orchestrated pipelines and dbt transformation projects (modular, tested, documented).
• Help design analytics-ready models: SCD Type 2, star schemas, and appropriate normalization for upstream canonical layers.
• Learn and apply Master Data Management (MDM) patterns (golden records, reference data, deduping, identity resolution).
• Implement data quality checks (freshness, nulls, referential integrity, distribution drift, anomaly detection).
• Contribute to data governance habits: data stewardship, ownership, SLAs, and clear definitions for “source of truth.”
• Help build and maintain a business semantic layer (consistent metric definitions, dimensions, and reusable logic) used by notebooks/BI.
• Partner with stakeholders (Product, Engineering, Finance, GTM, Ops) to translate questions into durable datasets and metrics.
• Use SQL, Python, and Spark where scale demands it; optimize for correctness, performance, and cost.

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers and engineers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $130,000 - $170,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

Please see our privacy policy at https://www.together.ai/privacy  

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.

Bridge is creating an entirely new payments platform, built with stablecoins, to simplify global money movement. Bridge enables faster, cheaper payments and borderless access to dollars via stablecoins. Through our APIs, businesses can send and receive funds across borders faster / cheaper vs. SWIFT and other fiat-only rails. Our virtual accounts enable international consumers and businesses to easily access, store and spend US dollars. Our payouts infrastructure enables platforms to disburse USD to anyone globally. We believe many trillions of dollars will move and settle through stablecoin payment rails. Bridge is pulling this future forward.

About the team

The Stripe Finance & Strategy team is an integral thought partner to Stripe’s core functional leaders.  We bring understanding and predictability to Stripe’s bottom line financials. We work cross functionally across many surfaces at Stripe and help steward Stripe’s resource investments to improve the durability of our long term financial performance. This role will partner closely with Bridge, a small team of people experienced in building financial infrastructure at leading companies.

What you’ll do

We are looking for a Strategic Finance Partner who combines the rigor of a trained financial analyst with the technical self-sufficiency to answer complex business questions independently. This is a high-ownership, high-exposure role. You will own business forecasting, pricing analytics, and cross-functional partnerships that drive how Bridge allocates capital, sets prices, and grows.

This is an early and consequential moment to join the finance function. You will be among the first finance hires in the organization and will have broad exposure across Bridge and Stripe leadership.

Responsibilities

• Forecasting & Reporting. Own full P&L reporting, forecasting, and budgeting — with an emphasis on revenue, unit economics, and the stablecoin payment flows that drive Bridge's business.
  
  
• Pricing & Deal Modeling. Lead pricing strategy across Bridge's product suite. Build models to evaluate pricing for enterprise deals, key accounts, and new product initiatives. Translate complex stablecoin transaction economics into pricing decisions that are both competitive and financially sound.
  
  
• Strategic Financial Analysis. Partner with business leaders across Bridge to analyze the financial impact of strategic initiatives, understand their business questions and deliver analytical frameworks that drive real decisions.  
  
  
• Metrics & Analytics. Partner with Bridge's data science team to define, build, and maintain the key business and operational metrics that measure Bridge's growth and efficiency. You own the financial lens on this work.
  
  
• Data & SQL Ownership. Write and maintain complex SQL queries and views to support P&L reporting, forecasting, and ad-hoc financial analysis. Work with engineering to ensure accuracy of financial metrics. Bridge has built strong infrastructure in Metabase and Databricks — you leverage it and go beyond it independently, without filing tickets and waiting.
  
  
• Stripe Finance Partnership. Integrate Bridge financials into broader Stripe company financial reporting and forecasting. Evolve the finance partnership and reporting structure as Bridge and Stripe develop joint product initiatives.

Who you are

We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• 7+ years in strategic finance, FP&A, investment banking, private equity, consulting, or analytical business operations. We care about the rigor of your training, not the specific industry.
• Strong financial modeling fundamentals — you build 3-statement models, unit economics frameworks, and cohort analyses from a blank spreadsheet. You understand the assumptions behind the numbers, not just the formulas.
• Genuine SQL proficiency. You write your own queries when the dashboard doesn't have what you need. Experience building data tables in Databricks or similar environments is a strong plus.
• A demonstrated track record of cross-functional partnership — you have sat with non-finance leaders, understood the actual problem they were trying to solve, and built something that improved their decision-making.
• Intellectual curiosity and drive. Stablecoin payments infrastructure is genuinely complex. We need someone who will spend real time understanding how the business works — the settlement flows, the transaction economics, the cross-border mechanics — not skim the surface.
• Self-directed, organized, and a strong communicator. You own your outcomes.
• High performance under tight timelines. A roll-up-your-sleeves, get-it-done mentality that fits a small team building something new.

Preferred qualifications

• Prior experience in fintech, payments, or crypto — particularly in transaction-based or stablecoin business models.
• Familiarity with Databricks, dbt, Looker, or Metabase.
• Experience in a first-finance-hire or early-stage company context.
• Exposure to enterprise deal structuring or pricing in a payments or API-product business.

Data at Brex

The Data organization develops insights, models, and data infrastructure for teams across Brex, including Sales, Marketing, Product, Engineering, and Operations. Our Data Scientists, Analysts, and Engineers work together to make data—and insights derived from data—a core asset across the company.

What you’ll do

As a senior Data Analyst (DA III), you will own the end-to-end analytics lifecycle for one or more business areas at Brex. You’ll go beyond building dashboards—you’ll frame the right questions, design rigorous analyses, apply statistical methods, and translate your findings into clear recommendations for leadership. You will also serve as a technical leader on the Data Analytics team, mentoring more junior analysts and helping define the standards and best practices that elevate the team’s work.

This role sits at the intersection of analytics, analytics engineering, and business strategy. You’ll work in a modern data stack environment and partner closely with Data Scientists, Data Engineers, and senior leaders across the organization.

Where you’ll work

This role will be based in our San Francisco office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Own the analytics lifecycle for assigned business areas: from problem framing and data sourcing through analysis, insight generation, and stakeholder presentation.
• Build and maintain dashboards and self-service reporting tools that enable business teams to independently track performance, identify risks, and make data-driven decisions.
• Write production-quality SQL and Python code to extract, transform, and analyze data at scale.
• Collaborate with Data Engineers and Data Scientists to develop and maintain analytical data models, improve data pipelines, and ensure data quality across the organization.
• Partner with leadership across Sales, Operations, Product, Finance, and other departments to identify high-impact analytical opportunities and deliver actionable recommendations.
• Mentor other data analysts and contribute to the development of team standards, documentation, code review practices, and analytical frameworks.
• Proactively identify gaps in data infrastructure, propose improvements, and contribute to the evolution of the team’s tooling and processes.

Requirements

• 5+ years of experience in data analytics, business intelligence, or a related quantitative role.
• 3+ years of experience partnering directly with Sales, Operations, Product, or equivalent business teams as an embedded analytics partner.
• Advanced SQL proficiency, including CTEs, window functions, performance optimization, and working across complex data models.
• Proficiency in Python for data analysis, automation, and modeling (Pandas, NumPy, scikit-learn, or similar).
• Experience with cloud data warehouses, particularly Snowflake (BigQuery and Databricks also valued).
• Hands-on experience with BI and data visualization tools (Looker, Tableau, Hex, or similar).
• Strong stakeholder management skills—proven ability to present complex technical findings to non-technical audiences.
• Experience with generative AI and LLM-based tools (Claude Code, Cursor, GitHub Copilot) to perform and accelerate analyses, automated reporting, and build self-service data tools.

Bonus points

• Demonstrated experience applying statistical methods to business problems (e.g., regression, classification, A/B testing).
• Experience with dbt for data modeling and transformation.
• Experience building and maintaining data pipelines using orchestration tools such as Airflow.
• Experience working with APIs for data ingestion and integration.
• Familiarity with version control systems (Git).
• Experience in fintech, financial services, or payments.
• Track record of leading cross-functional analytics projects from scoping through delivery.

Compensation

The expected salary range for this role is $114,192 - $142,740. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity. Depending on the position offered, equity and other forms of compensation may be provided as part of a total compensation package.

Brex LLC is a wholly owned subsidiary of Capital One, N.A.

Data at Brex

Our Scientists and Engineers work together to make data — and insights derived from data — a core asset across Brex. But it's more than just crunching numbers. The Data team at Brex develops infrastructure, statistical models, and products using data. Our work is ingrained in Brex's decision-making process, the efficiency of our operations, our risk management policies, and the unparalleled experience we provide our customers.

What You’ll Do

As a Data Engineer at Brex, you will be a core contributor in transforming raw data into actionable insights for various departments across the organization. You'll collaborate closely with Data Scientists, Software Engineers, and business units to create efficient data models, pipelines, and analytics frameworks that drive the business forward. You also play a leading role in the design, implementation, and maintenance of Core Data tables, our high-quality, curated data source for a wide range of analytic applications. 

Where you’ll work

This role will be based in our Sao Paulo office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities:

• Design, build, and maintain data models and pipelines that scale with the growing number of services, products, and changes in the company.
• Collaborate closely with Data Scientists, Data Analysts, and Business teams to understand their data needs, translating them into robust, efficient, scalable data solutions that enable ease of predictive analytics, data analysis, and metrics formulation. 
• Maintain data documentation and definitions, building and ensuring that source-of-truth tables remain high quality for data science and reporting applications. 
• Develop and enable integration with various data sources, allowing for more data-driven initiatives across the company.
• Apply best practices in data management to ensure the reliability and robustness of data utilized across various analytics applications.
• Set and proliferate company-wide standards for data relating to structure, quality, and expectations.
• Act as a liaison between the technical and non-technical teams, bridging gaps and ensuring that data solutions align with business objectives.
• Use agentic AI where it speeds up pipeline and quality work, without skipping validation.

Requirements:

• 3+ years of experience in Data Engineering, Data Analytics, or a related field such as Analytics Engineering.
• Advanced knowledge of databases and SQL with the ability to efficiently stage, process, and transform data.
• Experience integrating and orchestrating data workflows with various modern data tools and systems.
• Experience with data modeling, ETL/ELT processes, and data warehousing solutions.
• Experience working with a data warehouse such as Snowflake.
• Experience with a data workflow orchestrator tool such as Airflow.
• Experience with a programming language such as Python. 
• Experience with agentic AI. 
• Exceptional quantitative and analytical skills.
• Strong communication skills and ability to collaborate with various stakeholders, both technical and non-technical.

Nice to have:

• Familiarity with BI tools such as Looker, Tableau, or similar platforms. 
• Solid dbt in production: models, tests, docs, and collaboration in a shared repo. 
• AI-ready data: Metrics and dimensions are clear and reusable so AI-assisted analysis does not fork definitions. 
• Experience with Salesforce data integration. 

Brex LLC is a wholly owned subsidiary of Capital One, N.A.

Data at Brex

Our Scientists and Engineers work together to make data — and insights derived from data — a core asset across Brex. But it's more than just crunching numbers. The Data team at Brex develops infrastructure, statistical models, and products using data. Our work is ingrained in Brex's decision-making process, the efficiency of our operations, our risk management policies, and the unparalleled experience we provide our customers.

What You’ll Do

As a Data Engineer at Brex, you will be a core contributor in transforming raw data into actionable insights for various departments across the organization. You'll collaborate closely with Data Scientists, Software Engineers, and business units to create efficient data models, pipelines, and analytics frameworks that drive the business forward. You also play a leading role in the design, implementation, and maintenance of Core Data tables, our high-quality, curated data source for a wide range of analytic applications. 

Where you’ll work

This role will be based in our San Francisco office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities:

• Design, build, and maintain data models and pipelines that scale with the growing number of services, products, and changes in the company.
• Collaborate closely with Data Scientists, Data Analysts, and Business teams to understand their data needs, translating them into robust, efficient, scalable data solutions that enable ease of predictive analytics, data analysis, and metrics formulation. 
• Maintain data documentation and definitions, building and ensuring that source-of-truth tables remain high quality for data science and reporting applications. 
• Develop and enable integration with various data sources, allowing for more data-driven initiatives across the company.
• Apply best practices in data management to ensure the reliability and robustness of data utilized across various analytics applications.
• Set and proliferate company-wide standards for data relating to structure, quality, and expectations.
• Act as a liaison between the technical and non-technical teams, bridging gaps and ensuring that data solutions align with business objectives.
• Use agentic AI where it speeds up pipeline and quality work, without skipping validation.

Requirements:

• 3+ years of experience in Data Engineering, Data Analytics, or a related field such as Analytics Engineering.
• Advanced knowledge of databases and SQL with the ability to efficiently stage, process, and transform data.
• Experience integrating and orchestrating data workflows with various modern data tools and systems.
• Experience with data modeling, ETL/ELT processes, and data warehousing solutions.
• Experience working with a data warehouse such as Snowflake.
• Experience with a data workflow orchestrator tool such as Airflow.
• Experience with a programming language such as Python. 
• Experience with agentic AI. 
• Exceptional quantitative and analytical skills.
• Strong communication skills and ability to collaborate with various stakeholders, both technical and non-technical.

Nice to have:

• Familiarity with BI tools such as Looker, Tableau, or similar platforms. 
• Solid dbt in production: models, tests, docs, and collaboration in a shared repo. 
• AI-ready data: Metrics and dimensions are clear and reusable so AI-assisted analysis does not fork definitions. 
• Experience with Salesforce data integration. 

Compensation:

The expected salary range for this role is $120,800 - $151,000. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity. Depending on the position offered, equity and other forms of compensation may be provided as part of a total compensation package.

Brex LLC is a wholly owned subsidiary of Capital One, N.A.

About this role

We are looking for an experienced Senior Business Intelligence Analyst to scale a critically important function responsible for building and maintaining systems, processes and services that enable us to understand financial performance and unlock key business insights at Faire. The ideal candidate for this role will ensure key accounting procedures are automated in ways that are accurate, efficient, easy-to-understand, and scalable. This role will sit in the Finance department and collaborate with team members from a breadth of functional groups across Faire, including Accounting, Finance & Strategy, Engineering and Data Infrastructure. This role will have a tremendous impact on the ability for Faire employees to use data effectively in order to report numbers externally and make impactful decisions internally. As an early member of a burgeoning team, you will have the opportunity to write the roadmap for how we improve access to business critical data and unlock automation that amplifies the impact of a variety of functions. 

What you’ll do 

• Scope, design, create, and manage complex ETL flows that aggregate and synthesize key business performance metrics and financial reporting, ensuring efficient, accurate, and scalable data models using best practices 
• Act as a trusted analytical partner to the Accounting and Finance teams providing technical expertise, gathering requirements from product and operational teams, and enabling data-driven decision-making and accountability by ensuring core ledgers accurately reflect business changes 
• Collaborate closely with Engineering, Data Infrastructure, and other stakeholders as a key translator and process improver, gathering requirements and delivering impactful data solutions that ensure alignment and efficiency across the organization 
• Lead initiatives to drive data strategy, roadmap planning, and prioritization, ensuring alignment with business objectives 
• Act as a thought leader to expand the team’s influence and impact throughout the Finance team and the broader organization 
• Drive workflow automation and innovation across Finance, including identifying and implementing Gen-AI–enabled productivity solutions to modernize processes and increase team leverage

Qualifications

• 5+ years of work experience in Business Intelligence, Analytics Engineering, Data Analytics, or comparable function 
• Expert in SQL with focus on data modeling, large-scale data processing, and tool development for analytics or Financial use cases. 
• Experience and knowledge of data warehousing concepts, ETLs, big data technologies, and analytics platforms 
• Experience with analytics workflow tools (examples: Airflow, Docker, dbt), data warehousing and storage (examples: Snowflake, BigQuery, Redshift, S3), and data visualization tools (examples: Mode, Tableau, Looker) 

Nice to have: 

• Experience with Python, R or other programming languages 
• BA/BS degree, ideally in computer science, math or another quantitative field 
• Familiarity with version control using Git and proficiency in command-line tools for workflow automation and data manipulation

Salary Range

San Francisco and New York: The pay range for this role is $174,500 to $240,000 per year. 

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.

As a Senior Analyst, Analytics engineering, you’ll enable our analytics community to get the most out of our data. You’ll support analysts of varying levels of scope and experience by building, maintaining, and governing a data warehouse of operational and financial data.

You’ll keep us on the cutting edge of AI-driven analytics by partnering with Data Ops to develop a semantic layer and various agents to facilitate faster, better, and more interpretable insights.

We're seeking someone based near one of the following cities: San Francisco, Salt Lake City, Chicago, or New York City.

RESPONSIBILITIES

• Develop and maintain data models in Snowflake with dbt
• Design and build a testing and alerting framework to improve data quality
• Create data governance/democratization policies to support the broader analyst community
• Design and build a self-service analytics AI framework, including both the semantic modeling and tooling architecture

Partner with Data Engineering to optimize data staging in Snowflake

QUALIFICATIONS 

• 4+ years of experience in data engineering, analytics, or data science
• A bachelor’s degree in Computer Science, Information Systems, Statistics, or another quantitative field
• Advanced SQL knowledge (experience with dbt is a plus)
• Moderate Python knowledge (JavaScript knowledge is a plus)
• Experience building complex data pipelines using engineering best practices in platforms like Airflow or Prefect

Strong organizational discipline and quality assurance skills

PERKS & BENEFITS 

• We offer Gongsters a variety of medical, dental, and vision plans, designed to fit you and your family’s needs.
• Wellbeing Fund - flexible wellness stipend to support a healthy lifestyle.
• Mental Health benefits with covered therapy and coaching.
• 401(k) program to help you invest in your future.
• Education & learning stipend for personal growth and development.
• Flexible vacation time to promote a healthy work-life blend.
• Paid parental leave to support you and your family.
• Company-wide recharge days each quarter.
• Work from home stipend to help you succeed in a remote environment.

The annual salary hiring range for this position is $120,000 - $145,000 USD. 

Compensation is based on factors unique to each candidate, including, but not limited to, job-related skills, qualification, education, experience, and location. At Gong, we have a location-based compensation structure, which means there may be a different range for candidates in other locations. The total compensation package for this position, in addition to base compensation, may include incentive compensation, bonus, equity, and benefits. Some of our sales compensation programs also offer the potential to achieve above targeted earnings for those who exceed their sales targets. 

We are always looking for outstanding Gongsters! So if this sounds like something that interests you regardless of compensation, please reach out. We may have more roles for you to consider and would love to connect.

We have noticed a rise in recruiting impersonations across the industry, where scammers attempt to access candidates' personal and financial information through fake interviews and offers. All Gong recruiting email communications will always come from the @gong.io domain. Any outreach claiming to be from Gong via other sources should be ignored.

Gong is an equal-opportunity employer. We believe that diversity is integral to our success, and do not discriminate based on race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, military status, genetic information, or any other basis protected by applicable law.

To review Gong's privacy policy, visit https://www.gong.io/gong-io-job-candidates-privacy-notice/ for more details.

#LI-NK1

Scale's Finance Analytics and Data Science team is looking for an experienced data leader to manage a team of analysts and data engineers building internal tools and AI agents that automate workflows across finance and accounting.

In this role, you will lead a small team while staying technically hands-on, working closely with stakeholders across business finance, corporate finance, and accounting to understand their workflows and build systems that enable faster turnaround times with higher degrees of accuracy.

You will guide your team in designing data models, prototyping internal tools, and implementing agent-driven workflows using our internal data infrastructure, system integration tooling, Scale's proprietary AI platform, and emerging AI tools.

We're looking for someone who enjoys solving complex problems, building systems from the ground up, translating ambiguous business needs into scalable technical solutions, and developing the people around them.

Key Responsibilities

• Lead and grow a small team of analysts and engineers, providing technical mentorship, career development, and day-to-day guidance
  
  
• Design and develop end-to-end agent-driven workflows that automate finance and accounting processes, and improve operational efficiency
  
  
• Build scalable data models and pipelines that serve as the foundation for analytics assets (e.g., dashboards), agentic workflows and system automations
  
  
• Partner with stakeholders across finance, accounting, and customer operations to translate business requirements into technical requirements and system designs
  
  
• Collaborate with engineering teams in the development of internal tools to improve Scale’s financial system infrastructure
  
  
• Evangelize and support the adoption of AI-enabled automation across finance and accounting teams

Ideally, you have:

• 6+ years of experience in data analytics, analytics engineering, or data science role
  
  
• 1-3+ years of people management experience
  
  
• Expert knowledge of SQL and Python for data analysis and modeling
  
  
• Experience building internal tools, automation systems, or data products
  
  
• Experience building a reliable transformation layer and pipelines from ambiguous business processes using tools such DBT to create a foundational data models
  
  
• Experience partnering with engineering, and business stakeholders to automate manual workflows
  
  
• Strong problem-solving skills and the ability to operate independently in a fast-paced environment
  
  
• Excellent communication skills and experience working cross-functionally with business stakeholders
  
  
• Familiarity with finance or accounting terminology
  
  
• Experience with BI tools such as Tableau, Metabase, or Looker
  
  
• Familiarity with JavaScript and modern automation tooling is a plus but not required

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human evaluation and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition.

At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.

At the foundation of these products is the Platform Engineering team. As a Staff Software Engineer, you will define and drive both the architectural roadmap and implementation of core platforms and software systems. You will be responsible for providing high-level vision and driving adoption across the engineering org for orchestration, data abstraction, data pipelines, identity & access management, and underlying cloud infrastructure.  You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies.

Impact and Responsibilities:

• Architectural Vision: You will drive the design and implementation of foundational systems, acting as a bridge between high-level business goals and technical goals.
• Cross-Functional Leadership: You will collaborate with cross-functional teams to define and drive adoption of the next generation of features for our AI data infrastructure.
• Technical Ownership: You are responsible for proactively identifying and driving opportunities for organizational growth, driving improvements in programming practices, and upgrading the tools that define our development lifecycle.
• Technical Mentorship: You will serve as a subject matter expert, presenting technical information to stakeholders and providing the guidance to elevate the engineering culture across the company.

Ideally you’d have:

• 8+ years of full-time engineering experience, post-graduation with specialities in back-end systems.
• Extensive experience in software development and a deep understanding of distributed systems and public cloud platforms (AWS preferred).
• Demonstrated a track record of independent ownership and leadership across successful multi-team engineering projects
• Possess excellent communication and collaboration skills, and the ability to translate complex technical concepts to non-technical stakeholders.
• Experience working fluently with standard containerization & deployment technologies like Kubernetes, Terraform, Docker, etc.
• Experience with orchestration platforms, such as Temporal and AWS Step Functions.
• Experience with NoSQL document databases (MongoDB) and structured databases (Postgres).
• Strong knowledge of software engineering best practices and CI/CD tooling (CircleCI, ArgoCD).

Nice to haves:

• Experience with data warehouses (Snowflake, Firebolt) and data pipeline/ETL tools (Dagster, dbt).
• Experience scaling products at hyper-growth startups.
• Excitement to work with AI technologies.

About the role

As an Analytics Data Engineering Manager focused on Product, you will build and lead the analytics engineering team responsible for creating the data foundations that enable data-driven decision making across Anthropic’s Product organization.  You will oversee the development of scalable data solutions for Product pillars – including Consumer, Claude Code, Enterprise & Verticals, Growth, Platform Product – managing a team of analytics engineers and working closely with stakeholders across Data Science, Product, and Engineering to ensure teams have access to reliable, accurate metrics that can scale with our company’s growth.

In this role, you will balance hands-on technical leadership with people management, setting the strategic vision for product data foundations while developing and mentoring team members.  You will partner closely with Product Data Scientists, Product Managers, and Product Engineers to understand how users interact with Claude, how to measure product quality and growth, and how to transform raw event logs into insightful data marts that power product decisions.

Responsibilities:

• Build and scale the Product Analytics Engineering team, including hiring and mentoring a team of high-performing analytics engineers embedded with Product pillars

• Define and execute the strategic roadmap for product data foundations and analytics capabilities

• Oversee the design and implementation of scalable data pipelines, data models, and analytics solutions that transform raw product event logs into canonical datasets and insightful data marts

• Partner with Data Science, Product, and Engineering leadership to understand data needs and translate them into technical requirements

• Establish and maintain high data integrity standards, SLAs, alerting, and best practices for the team

• Drive the development of foundational data products, dashboards, and tools to enable self-serve analytics; partner with the Data Science team to build innovative data tools using Claude to scale data-driven decisions across Product teams

• Foster a culture of technical excellence, continuous learning, and data-driven decision making

• Serve as a technical thought leader for data modeling, ETL processes, and product analytics infrastructure

You might be a good fit if you have:

• 5+ years of experience managing analytics engineering or data engineering teams, preferably in a scaling startup environment

• 8+ years of total experience in analytics engineering, data engineering, or similar data-focused roles

• Deep expertise in data modeling, ETL pipelines, and data warehouse architecture

• Strong technical foundation with expertise in SQL, Python, dbt, and modern data stack tools

• Proven track record of building and leading high-performing teams

• Experience partnering with Data Science, Product, and Engineering leaders to deliver key product metrics and user behavior insights

• Demonstrated ability to balance strategic thinking with hands-on technical leadership

• Strong communication skills with the ability to translate complex technical concepts for diverse audiences

• Experience scaling analytics functions from early stage to maturity in rapidly changing environments

• Track record of establishing data governance, quality standards, and best practices

• A bias for action and urgency, not letting perfect be the enemy of the effective

• A “full-stack mindset”, not hesitating to do what it takes to solve a problem end-to-end

• A passion for Anthropic’s mission of building helpful, honest, and harmless AI

About the Role

As an Analytics Engineer, you will be an early member of the Data Science & Analytics team building the foundation to scale analytics across our organization. You will collaborate with key stakeholders in Engineering, Product, GTM and other areas to build scalable solutions to transform data into key metrics reporting and insights. You will be responsible for ensuring teams have access to reliable, accurate metrics that can scale with our company’s growth. You will also lead your own projects to enable self-serve insights to help teams make data-driven decisions. 

Responsibilities:

• Understand the data needs of stakeholder teams in terms of key data models and reporting, and translate that into technical requirements
• Define, build and manage key data pipelines in dbt that transform raw logs into canonical datasets
• Establish high data integrity standards and SLAs to ensure timely, accurate delivery of data
• Develop insightful and reliable dashboards to track performance of core metrics that will deliver insights to the whole company
• Build foundational data products, dashboards and tools to enable self-serve analytics to scale across the company
• Influence the future roadmap of Product and GTM teams from a data systems perspective
• Become an expert in our organization’s data models and the company's data architecture

You might be a good fit if you have:

• 5+ years of experience as an Analytics Data Engineer or similar Data Science & Analytics roles, preferably partnering with GTM and Product leads to build and report on key company-wide metrics.
• A passion for the company's mission of building helpful, honest, and harmless AI.
• Expertise in building multi-step ETL jobs, building robust data models through tooling like dbt; proficiency with workflow management platforms like Airflow and version control management tools through GitHub.
• Expertise in SQL and Python to transform data into accurate, clean data models.
• Experience building data reporting and dashboarding in visualization tools like Hex to serve multiple cross-functional teams.
• A bias for action and urgency, not letting perfect be the enemy of the effective.
• A “full-stack mindset”, not hesitating to do what it takes to solve a problem end-to-end, even if it requires going outside the original job description.
• Experience building an Analytics Data Engineering (or similar) function at start-ups. 
• A strong disposition to thrive in ambiguity, taking initiative to create clarity and forward progress.

Who We Are

EDO is the TV outcomes company. Our measurement platform connects convergent TV airings to the ad-driven consumer behaviors most predictive of future sales, helping advertisers, agencies, networks, streamers, and studios understand what works across linear and streaming TV.

We combine immediate engagement signals with decision science, vertical AI, and investment-grade data to help the advertising industry maximize media impact, optimize creative performance, and value every impression. EDO is headquartered in New York City and Los Angeles with office space in San Francisco. For employees residing full-time in NYC, SF, or LA, we have a hybrid work policy of three days in the office and two remote work days.

The Role

EDO is looking for a Senior Platform Engineer to help accelerate our shift toward a platform model. This is a senior IC role on the Infrastructure team focused on paved paths, reusable infrastructure patterns, and self-service workflows that help software, data, and machine learning engineers ship safely and efficiently.

Our environment is AWS-heavy and data-intensive, with heavy use of EC2, RDS, S3, SageMaker, MWAA/Airflow, Snowflake, dbt, Chef, Terraform, Spacelift, and emerging ECS/container patterns. The right person has strong AWS and infrastructure-as-code fundamentals, prior exposure to platform engineering practices, and the pragmatism to modernize existing systems without assuming everything should be rebuilt from scratch.

We approach infrastructure as an internal product: understanding engineers’ needs, prioritizing high-leverage platform capabilities, and iterating on tools and workflows that make teams faster, safer, and more self-sufficient.

What You Will Do

• Build and evolve EDO's internal platform capabilities: paved paths, reusable infrastructure modules, deployment standards, documentation, and self-service workflows.
• Own and improve Terraform and Spacelift-based infrastructure workflows, including module design, reviews, approvals, drift detection, and guardrails.
• Modernize existing AWS infrastructure, including EC2- and Chef-based systems, while helping teams adopt more standardized and repeatable patterns over time.
• Support production data and ML platform infrastructure, including MWAA/Airflow, Snowflake, dbt, SageMaker, S3, RDS, and related AWS services.
• Partner with software, data, and ML engineers as internal customers, turning recurring infrastructure needs into scalable platform capabilities rather than one-off support work.
• Improve deployment, CI/CD, observability, alerting, and incident response practices so teams can operate production systems with greater confidence.
• Expand pragmatic ECS/container deployment patterns where they create value, without treating Kubernetes as the default answer.
• Participate in a lightweight on-call rotation and use operational learnings to reduce toil, improve reliability, and strengthen the platform.

What We Are Looking For

• 6+ years of experience in platform engineering, infrastructure engineering, SRE, DevOps, cloud infrastructure, or similar roles.
• Experience building, operating, or contributing to an internal developer platform, infrastructure platform, developer productivity team, or similar platform-oriented function.
• Strong production experience with AWS, especially EC2, RDS, S3, IAM, networking, load balancing, and related operational patterns.
• Strong hands-on experience with Terraform or similar infrastructure-as-code tooling, including reusable modules and multi-environment patterns.
• Comfort working in a data-heavy environment and supporting technologies such as Airflow/MWAA, Snowflake, dbt, SageMaker, S3, and RDS.
• Product-minded approach to infrastructure: able to understand engineer pain points, prioritize high-impact platform work, document paved paths, and iterate based on adoption and feedback.
• Strong operational judgment, including debugging production systems, improving observability, and building platform patterns that support reliability, security, and cost efficiency.

Bonus Points

• Hands-on experience with Spacelift or similar IaC orchestration platforms such as Terraform Cloud or Atlantis.
• Experience helping an organization adopt platform engineering practices, including paved paths, golden paths, service templates, and developer-facing documentation.
• Experience with Chef, AWS cost management, security/compliance-driven infrastructure, or production ML/data science infrastructure.

Compensation and Benefits

EDO offers a competitive compensation package. Components of compensation include:

• Mid-stage equity and competitive salary
• Flexible Time Off
• Medical, dental and vision coverage: EDO provides full coverage for individual medical plans and partial coverage for dependent or family plans.
• 401(k) plan, FSA, HSA
• Commuter Benefits
• When in an office, employee meals, snacks, and more

In compliance with New York and California Pay Transparency Law, the salary range for this position is $170,000 to $190,000 plus equity. We note the salary information as a general guideline only, as actual compensation may vary from posting. We will consider various factors to determine the offer for this role, including the scope and responsibilities of the position, relevant work experience, key skills, training, and business considerations.

EDO is an Equal Opportunity Employer. We do not discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status, or any legally protected status. All your information will be kept confidential according to EEO guidelines.

About the Role

This is the first dedicated data hire on Together AI's Finance team. You will own the data layer that Finance runs on — building from scratch the models, pipelines, and reporting infrastructure that allow Strategic Finance, FP&A, and Accounting teams to get reliable answers quickly. The person in this role will have direct exposure to every part of the Finance organization and a real opportunity to shape how data-driven decision-making develops here as the company scales.

A significant portion of your work will touch the data behind the economics of Together's infrastructure, which sits behind nearly every financial question we ask. You will work closely with Together's Data and Commerce engineering team, which owns the underlying billing pipelines and data warehouse. Your job is to define and build the modeling and reporting layer that turns raw operational data into finance-grade datasets — aligning on data contracts, representing Finance's requirements in data design decisions, and ensuring the metrics Finance depends on are correct, documented, and trusted.

Responsibilities

• Own and evolve the dbt transformation layer for Finance: design, build, test, document, and maintain models covering billing, financial performance, compute unit economics, and operational metrics
• Author and maintain Airflow DAGs (Astronomer-managed) that orchestrate Finance dbt runs, data quality checks, and downstream dependencies reliably
• Deliver dashboards and reporting in Hex for the executive team covering financial performance, utilization, and key operating metrics
• Partner with Strategic Finance, FP&A, and Accounting teams on the data infrastructure behind forecasting, cost modeling, and other financial analyses
• Set data quality standards across Finance data products and own incident response when Finance-critical pipelines break
• Build the data foundation – clean, well-structured, documented, and reliably maintained – that enables Finance team to self-serve data analysis and supports AI automation and agentic workflows 

Requirements

• 5+ years in an analytics engineering or data engineering role, with meaningful time supporting Finance or business operations teams
• Expert SQL and production-grade dbt experience — models, tests, docs, snapshots, incremental strategies; you've designed a dbt project from scratch and maintained it
• Hands-on experience with Snowflake and Airflow in production; familiarity with Astronomer and the Cosmos dbt integration is a strong plus
• Solid dimensional modeling fundamentals: star schemas, slowly changing dimensions, fact table grain — you can apply Kimball principles without needing to look them up
• Strong dashboarding skills with a track record of executive-facing reporting that people actually use; experience with Hex or a comparable notebook/BI tool
• Clear communicator who can align stakeholders on metric definitions and translate between engineering tradeoffs and Finance requirements in both directions
• High comfort with ambiguity; this role will require scoping problems from scratch, not executing against pre-defined specs

Nice to Have

• Experience with financial data or billing data such as ARR, usage-based billing, invoice reconciliation, revenue recognition patterns, etc.. We operate a usage-based billing system and this context transfers directly.
• Experience with PII handling, data masking, access-tier modeling, or compliance work (SOC 2, ISO 27001, GDPR, CCPA).
• Familiarity with lakehouse patterns (Iceberg, Delta, Hudi) and hybrid warehouse/lake architectures.
• Python for data tooling: automation, data quality frameworks, custom dbt macros or operators.
• Experience with Hex, Metabase, or similar notebook/BI tooling that sits on top of your dbt models.
• Prior experience in a high-growth AI/ML infrastructure or platform company

About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.

Compensation

We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200k -$240k + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.

Equal Opportunity

Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

Please see our privacy policy at https://www.together.ai/privacy.  

fal is the generative media ecosystem powering the next generation of AI products. We build the infrastructure, tools, and model access that teams need to move from idea to production, and do it at scale without compromise. For developers and enterprises, fal is the foundation that makes generative media not just possible, but practical: a unified platform where high-performance inference, orchestration, and observability come together to unlock new categories of AI-native products.

As generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on.

About this role: 

As a Senior Data Scientist for Go-to-Market at fal, you will be the analytical backbone of our revenue organization. Embedded directly with the GTM function, you will own the metrics that tell us how our pipeline is performing, how our sales team is executing, and where the next dollar of revenue is most likely to come from.

This is a high-leverage, high-visibility role. GTM leadership will look to you for the answers - on rep performance, quota attainment, pipeline health, AM coverage, and segment economics - and you'll shape both the questions we ask and the systems we build to answer them. You'll partner closely with Product Intelligence and Data Engineering as part of fal's center-of-excellence data team, while acting as the embedded specialist for everything GTM.

Key responsibilities:

• Own the metrics and reporting that drive sales execution at fal: pipeline health, quota attainment, conversion rates, sales cycle, AM coverage, and segment-level economics.
• Partner directly with the sales leadership and rev-ops to translate strategic questions into measurement frameworks and weekly operating cadences.
• Build the data foundations that make GTM analytics first-class: account-manager tracking, territory and quota models, opportunity-stage instrumentation, and rep-level performance views.
• Run rigorous deep-dives - win/loss, lead source effectiveness, segmentation, expansion drivers - that change how GTM allocates time and headcount.
• Shape the CRM, internal tooling, and downstream data models that everyone in GTM depends on, and set the standards for how GTM data is captured, joined, and trusted.

Requirements:

• 5+ years of experience in data science or analytics roles, with at least 3+ years specifically in GTM, RevOps, or sales analytics at an enterprise or B2B SaaS company. This is a hard requirement - we are looking for someone who has seen many variations of this problem and can run with it.
• Advanced SQL and proficiency in Python for analytics, modeling, and experimentation.
• Deep familiarity with Salesforce data, pipeline mechanics, quota and territory models, and the operational rhythms of an enterprise sales org.
• Strong proficiency with dbt and modern analytics stacks; comfort partnering with data engineering on production-grade pipelines.
• Proven ability to operate as an embedded analytics partner - building trust with sales leadership and translating ambiguous strategic asks into clear measurement.
• Demonstrated bias for action; you set up the dashboard, write the SQL, and ship the framework yourself when needed.
• A track record of shipping data products that change behavior, not just dashboards that get viewed once.

Nice to have:

• Experience supporting both self-serve/PLG and enterprise sales motions.
• Experience working on developer-facing or API products.
• Familiarity with usage-based pricing and consumption-driven sales motions.
• Early-stage or fast-scaling environment experience.

Compensation

• $180,000-225,000 plus equity + benefits (This range is across 2 levles Senior and Staff)

Location

• San Francisco, CA (willing to consider remote for Senior and Staff levels)

What we offer at fal

• Interesting and challenging work

• A lot of learning and growth opportunities

• We are currently hiring in downtown San Francisco.

• We offer relocation assistance to San Francisco.

• Health, dental, and vision insurance (US)

• Regular team events and offsites

We are looking for a Senior Data Engineer to join our Data & ML Platform team, responsible for development, maintenance and support for Oura’s Datalake and adjacent systems, collaborating closely with data scientists, analysts and other departments utilizing the Datalake.

This is a hybrid role based in San Francisco, CA, working onsite 1-2 days per week.

We have offices in San Francisco, San Diego and Los Angeles for those who prefer hybrid or office settings. Oura employees in other major cities (like Boston and New York) occasionally gather informally at local co-working locations.

What you will do: 

• Build, operate, and improve Oura’s Datalake consisting of systems to move, process, and store large amounts of data (Terabyte–Petabyte scale) leveraging tools such as AWS Kinesis, S3, Spark / Glue, Athena, dbt, Iceberg, Snowflake, Docker, workflow engines, and more.
• Design and implement data models and pipelines with a strong focus on data quality, reliability, and governance.
• Ensure that our data platform and processes meet privacy, compliance, and security requirements.
• Build components that support the handling of datasets, training, testing, and release of new on-device and cloud-based ML models.
• Independently collaborate with different stakeholders including Data Scientists, Testing, Firmware, and Hardware to define and implement improvements and new functionality and support internal Datalake consumers in their day-to-day work.
• Write code, mostly in Python and SQL  with occasional additions Typescript, Java or some other language.

We would love to have you on our team if you have:

• 5+ years of experience developing and operating production systems.
• Experience running, monitoring, and debugging production systems at scale on a public cloud. We rely on AWS but experience with other cloud platforms counts too.
• Good architectural understanding of event-driven architectures, workflow engines, database, and data warehouse systems.
• Ability to design robust data models and pipelines, with attention to data accuracy and integrity.
• Experience in building infrastructure and components following best practices such as CI/CD and infrastructure as code.
• Demonstrated skills in writing well-structured, testable, and high-performance code.
• Familiarity with some of the following: Workflow engines, Stream processing, MLops, Spark, Athena, SQL, dbt, Databricks.
• You are self-motivated, proactive, and able to lead and manage your own work effectively.
• A pragmatic and goal oriented approach to development.

What Makes You Stand Out

• Real-life experience with Databricks.
• Familiarity and experience with the Data Mesh approach
• Broad knowledge of software fundamentals, databases, warehouses, and system design with experience on various programming languages

Benefits 

At Oura, we care about you and your well-being. Everyone here at Oura has a ring of their own and we are continually looking to improve employee health.

What we offer:

• Competitive salary and equity packages
• Health, dental, vision insurance, and mental health resources
• An Oura Ring of your own plus employee discounts for friends & family
• 20 days of paid time off plus 13 paid holidays plus 8 days of flexible wellness time off
• Paid sick leave and parental leave

Oura takes a market-based approach to pay, which may vary depending on your location. US locations are categorized into tiers based on a cost of labor index for that geographic area. While most offers will be closer to the starting range, successful candidates' pay will be determined based on job-related skills, experience, qualifications, work location, internal peer equity, and market conditions. These ranges may be modified in the future.

• Region 1 $172,550 - $203,000
• Region 2 $159,000 - $187,000
• Region 3 $148,000 - $174,000

A recruiter can determine your zones/tiers based on your US location.

About the role

Starburst is looking for an experienced Staff Software Engineer to sit at the intersection of software engineering and data engineering — building the internal platforms, pipelines, and data products that power how we understand our customers and our business. You'll work across Starburst Enterprise and Starburst Galaxy, using our own technology to solve real problems at scale, while also contributing software that other teams depend on.

Because data engineers are one of our primary target personas, you'll bring a uniquely valuable perspective: you'll use our products daily and feed that experience directly back into how we build and improve them. You'll work closely with a dynamic, talented, globally distributed team, writing production-quality software alongside building reliable data systems — with a consistent emphasis on quality, maintainability, and speed to insight.

As a Staff Software Engineer at Starburst you will:

• Design, build, and operate a high-quality data lake and the software systems that feed it, supporting product management, finance, customer support, and engineering
• Write production-grade software — services, APIs, tooling, and automation — that enables data workflows and integrations at scale
• Find innovative ways to use Trino and Starburst to solve complex data management and software architecture challenges
• Collaborate with technical leads, product managers, and data analysts to build robust data products and analytics
• Leverage AI to democratize access to datasets for users throughout Starburst, including building and maintaining the software infrastructure that makes this possible
• Enable dataset preparation and model evaluation for Starburst's AI projects
• Define and evolve engineering processes and best practices that span both software and data domains, with a focus on reliability and actionable results
• Work closely with leaders across teams to iterate on data architecture and the software systems that implement it, keeping quality high at every layer
• Identify emerging patterns in data management and software engineering, and work with Starburst's product teams to bring those innovations to market

Some of the things we look for

• A Trino enthusiast looking to push the bounds in an AI future
• At least 7 years of combined experience in software engineering and/or data engineering, with genuine depth in both
• Strong software engineering fundamentals: writing clean, testable, well-reviewed code in Java, Python, or Scala
• Experience building and optimizing data pipelines using Trino, Spark, dbt, and related frameworks
• Experience designing and building backend services, APIs, and data platform components
• Experience managing data and application infrastructure in public clouds, particularly AWS (EMR, EC2, S3, IAM); multi-cloud experience (GCP/Azure) is a plus
• Experience using and managing orchestration frameworks such as Apache Airflow or Dagster
• Knowledge of RAG and other design patterns for AI-powered applications
• Fluency in SQL and comfort moving between SQL and general-purpose programming as the task demands
• Experience building API integrations for extracting data from third-party sources
• Knowledge of data modeling techniques appropriate for modern data lakes
• Proficiency with Configuration-as-Code and Infrastructure-as-Code tools such as Pulumi, Terraform, and/or Ansible
• A track record of shipping reliable software and data systems on time, with disciplined practices around testing, code quality, and review
• Strong written and verbal communication skills, with the organizational ability to coordinate across teams

Where could this role be based?

• San Francisco, US

Location: San Francisco, CA (Hybrid) 

What is Verse? 

Energy markets are more volatile than ever. Rapid electrification and the rise of AI are driving unprecedented demand for power, while energy costs continue to rise across the globe. For the world’s largest energy buyers, managing energy has never been more complex or more critical.

Verse helps these organizations manage complex power portfolios with confidence by unifying energy data, planning, forecasting, and operations in one tool. Our Energy Cost Intelligence platform, Aria, brings together energy, finance, and operations teams with real-time, finance-ready intelligence—replacing spreadsheets and consultants with precision across the entire energy lifecycle. Built by an expert team of energy buyers, data scientists, and engineers, Verse enables faster, smarter energy decisions that reduce risk and lower energy costs.

The Role

Verse is seeking a Data Scientist to join our Data Science Team. In this role, you will lead the development and deployment of advanced data-driven solutions across a range of applications, including electricity markets, renewable procurement, and energy risk management. You will shape the machine learning and data modeling foundations that Verse's software is built on. For example, you might spend a cycle deploying electricity market price forecasting pipelines for new regions, developing solar production anomaly detection models, or creating scalable tools for benchmarking energy project financial performance.

Key Responsibilities

• Lead End-to-End Data Science Projects: Own and drive projects from problem definition through scoping, modeling, validation, and production deployment. Translate business problems into scalable, high-impact modeling solutions.
• Statistical & Machine Learning Modeling: Design, develop, and refine statistical and machine learning models (e.g., time series forecasting, probabilistic models) to support decision-making and enhance product capabilities.
• Analytics Engineering & Data Modeling: Perform complex data transformations and develop well-structured data models. Translate business and analytical requirements into scalable, tested, and well-documented datasets.
• Software Development & Productionization: Write clean, efficient, and maintainable Python code. Contribute to integrating models into production systems in a cloud-based environment while leveraging AI coding tools to accelerate development.
• MLOps: Contribute to Verse’s machine learning modeling infrastructure to support scaling of ML models and improving reliability, monitoring, and performance in production.
• Cross-Functional Collaboration: Partner with product, engineering, and business stakeholders to ensure models and insights are aligned with user needs and effectively integrated into workflows.

What We're Looking For (Minimum Qualifications)

• Master’s degree in Computer Science, Statistics, Engineering, Applied Mathematics, or a related quantitative field and 2+ years of professional experience in data science or machine learning; or
• Bachelor’s degree and 4+ years of professional experience in data science or machine learning
• Strong foundation in statistical modeling and machine learning, including time series forecasting and model evaluation
• Experience deploying and maintaining models in cloud-based environments (e.g., AWS, GCP, or Azure) using MLOps practices
• Strong Python expertise, including experience with scientific computing and ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch, TensorFlow)
• Hands-on experience in orchestrating complex data transformations (e.g. Airflow, Dagster, dbt)
• Strong software engineering practices (version control, testing, code reviews, CI/CD)
• Strong communication skills, with the ability to explain technical concepts to non-technical stakeholders

What Will Make You Standout (Preferred Qualifications)

• Experience in energy, climate tech, or related domains (not required)
• Familiarity with optimization methods or operations research
• Experience developing probabilistic forecasting models and quantifying uncertainty
• PhD in a quantitative field

What Makes Verse a Great Place to Work? 

• Lead with Empathy: We lift each other up with humility and kindness, always putting colleagues and customers first
• Be Honest & Transparent: We prioritize effective communication to build trust with our team, customers, and stakeholders
• Move with Balance & Precision: We believe speed and perseverance must be accompanied by thoughtfulness and reflection
• Leave the World a Better Place: We are passionate about our mission, and we strive to create a sustainable world for future generations

Base Pay Range

$146,000 - $172,000

This is the estimated base salary range for this position, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs.

Benefits and Employee Perks 

• Competitive compensation and equity grant at a high-growth start up 
• Comprehensive benefits package including medical, dental and vision insurance, and 401k 
• Flexible hours and unlimited PTO 
• Diverse and inclusive working environment 

Verse is an equal opportunity employer. All applicants and employees are considered for hire, promotion, and compensation without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, marital or familial status.

About the role

Are you passionate about using data to shape the future of Faire's product features for small and medium businesses (SMBs)? If so, we want to talk to you! We're looking for a technical leader in our team to work closely with Data Scientists, Product Analysts and Software Engineers to support product launches and roadmaps by designing and building data capabilities that inform and drive insights. In this role, you'll see a direct link between your work, company growth, and user satisfaction. You'll work with A tier players, work with one of the richest data sets in the world, use cutting-edge technology, and see your efforts affect products and SMBs regularly.

Join us and be part of a team that values data-driven innovation and excellence.

What You Will Do

The ideal candidate will possess a perfect blend of analytics and data engineering expertise, along with strong skills in developing and supporting large-scale, high-performance data storage and processing systems. They should have a proven track record of collaborating with key stakeholders to execute on company-wide data initiatives. 

• Lead cross-team and/or multi-phase projects from design through implementation, fostering collaboration with cross-functional partners.
• Lead the technical execution in building scalable data models and pipelines for a domain defining the architecture, feature requirements, roadmap.
• Build data pipelines with rigorous approaches to data quality, validation, testing, and observability.
• Optimize processes for operational excellence in project management and system reliability.
• Ensure security standards are met within data systems and workflows. 
• Ensure and promote data quality standards for accurate and reliable insights.
• Embrace emerging technologies with enthusiasm.

Minimum qualifications

• 4+ years experience in analytics & data engineering roles focused on data modeling, large-scale data processing, and tool development for analytics or data science use cases.
• Demonstrated communication and leadership skills, with a history of initiating and steering successful projects across multiple stakeholders.
• Production-level development experience in Python.
• Strong SQL skills with demonstrable competencies in designing well-architected data models and optimizing query performance.
• Deep experience and knowledge of data warehousing concepts, ETLs, big data technologies, and analytics platforms.
• Experience with Airflow, Docker, DBT or similar analytics workflow tools.
• BA/BS degree in Computer Science, Math, Physics, or a related technical field.

Salary Range

Toronto: the pay range for this role is $171,000 to $235,500 per year. 

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.

Faire uses Artificial Intelligence (AI) to screen and select applicants for this position.

This job posting is for an existing vacancy.

About the role

Are you passionate about using data to shape the future of Faire's product features for small and medium businesses (SMBs)? If so, we want to talk to you! We're looking for a technical leader in our team to work closely with Data Scientists, Product Analysts and Software Engineers to support product launches and roadmaps by designing and building data capabilities that inform and drive insights. In this role, you'll see a direct link between your work, company growth, and user satisfaction. You'll work with A tier players, work with one of the richest data sets in the world, use cutting-edge technology, and see your efforts affect products and SMBs regularly.

Join us and be part of a team that values data-driven innovation and excellence.

What You Will Do

The ideal candidate will possess a perfect blend of analytics and data engineering expertise, along with strong skills in developing and supporting large-scale, high-performance data storage and processing systems. They should have a proven track record of collaborating with key stakeholders to execute on company-wide data initiatives. 

• Lead cross-team and/or multi-phase projects from design through implementation, fostering collaboration with cross-functional partners.
• Lead the technical execution in building scalable data models and pipelines for a domain defining the architecture, feature requirements, roadmap.
• Build data pipelines with rigorous approaches to data quality, validation, testing, and observability.
• Optimize processes for operational excellence in project management and system reliability.
• Ensure security standards are met within data systems and workflows. 
• Ensure and promote data quality standards for accurate and reliable insights.
• Embrace emerging technologies with enthusiasm.

Minimum qualifications

• 4+ years experience in analytics & data engineering roles focused on data modeling, large-scale data processing, and tool development for analytics or data science use cases.
• Demonstrated communication and leadership skills, with a history of initiating and steering successful projects across multiple stakeholders.
• Production-level development experience in Python.
• Strong SQL skills with demonstrable competencies in designing well-architected data models and optimizing query performance.
• Deep experience and knowledge of data warehousing concepts, ETLs, big data technologies, and analytics platforms.
• Experience with Airflow, Docker, DBT or similar analytics workflow tools.
• BA/BS degree in Computer Science, Math, Physics, or a related technical field.

Salary Range

San Francisco: the pay range for this role is $196,000 to $269,500 per year.

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.

Faire uses Artificial Intelligence (AI) to screen and select applicants for this position.

This job posting is for an existing vacancy.

About ROLLER

ROLLER is not your average software-as-a-service company. With a global presence in over 30 countries, we're here to bring some excitement to the leisure and attractions industry and make a difference! Our mission is to help businesses operate smoothly and create fun and memorable guest experiences by providing seamless ticketing, point-of-sale, self-serve kiosks, memberships and digital waiver processes.

But here's the best part: our team. We're a group of 300+ highly passionate, enthusiastic, and down-to-earth professionals located all around the world who are all working together to build something truly remarkable. We're aiming high and believe that the possibilities are endless. As we continue to grow globally, we're excited to write our success story and have fun along the way.

We genuinely love what we do, and we're looking for like-minded people to join us on this amazing journey. If you're ready to be part of a dynamic team and make a real impact, come aboard, and let's create some unforgettable experiences together at ROLLER!

About the Role

We are seeking a highly technical, hands-on Senior Data Engineer to architect and scale our data infrastructure from the ground up. As a core member of the data function within our Business Technology team, you will be responsible for building the pipelines and models that power smarter decision-making and operational efficiency across the entire organization.

In this foundational role, you will build & architect the modern data stack, ensuring our infrastructure is robust, automated, and governed. You collaborate as a technical partner with GTM ,Product, Finance, P&C to build high-performance data models and pipelines that transform raw data into actionable cross-functional insights.

Joining ROLLER at this pivotal growth stage, you will have the opportunity to make an outsized impact on our technical landscape, directly influencing how we process data to deliver world-class experiences for our customers globally.

What You'll Do

• Scalable Architecture: Design and implement robust Data Modeling and Architecture strategies that support high-volume data processing and maintain long-term scalability.
• Cloud Ecosystem: Develop and manage end-to-end data pipelines using Google Cloud Platform services, specifically leveraging Cloud Run, Cloud Function for compute, BigQuery for storage, and Cloud Composer (Airflow) for orchestration.
• Transformation Excellence: Build, maintain, and document complex data transformation layers using DBT (Data Build Tool) to ensure high data quality and reliability.
• Infrastructure as Code: Automate the provisioning of cloud resources using Terraform and streamline deployment workflows via GitHub Actions CI/CD pipelines.
• Cloud Data Warehousing: Demonstrate expert-level proficiency in managing and optimizing Cloud Data Warehouses, such as Snowflake & BigQuery.
• Team Collaboration: Partner with cross-functional stakeholders to build and scale high-performing data infrastructure that supports engineering, analytics, and Data Program needs.
• Stack Management: Architect and maintain a modern data stack by implementing best-in-class tools including Snowflake, Bigquery, dbt, Fivetran, Looker, Tableau and Airflow.
• Pipeline Engineering: Develop scalable ETL/ELT workflows and robust data models to deliver real-time analytics and unified business reporting.
• Data Visualization: Create and maintain intuitive, high-performance Looker dashboards will be an added advantage.

About You

You are a technical builder who views data as a competitive advantage. You thrive in fast-paced environments and have a proven track record of engineering scalable data foundations from the ground up. Equal parts architect and analytical problem-solver, you excel at balancing long-term technical vision with the daily hands-on execution of complex pipelines.

You are energized by the full lifecycle of data—moving beyond just writing code to actively leading project discovery and analysis alongside cross-functional partners. You take pride in enabling GTM, Product, Finance & P&C teams through clear insights and high-performance systems. Most importantly, you are excited to act as a founding technical force for a high-growth SaaS company that is transforming the guest experience for attractions businesses globally.

• 5+ years of experience in Data & Analytics or Data Engineering with a focus on Scalable cloud driven data pipeline build

• Proven experience in dimensional modeling (Star/Snowflake schema) and designing architectures that support both historical reporting and real-time needs.
• Expert-level proficiency in Snowflake or BigQuery, including performance tuning, cost optimization, and access control (RBAC).
• Hands-on experience with Google Cloud Platform services, specifically Cloud Function, Cloud Run for containerized workloads and Cloud Composer for orchestration.

• Through hands-on experience of dbt (Data Build Tool) to manage transformations, including writing custom macros, implementing tests, and generating documentation.
• Solid experience using Terraform to provision cloud resources, ensuring that infrastructure is version-controlled and reproducible.
• Proficiency in GitHub Actions to automate testing and deployment pipelines for data models and infrastructure.
• Ability to design high-performance dashboards that prioritize user experience and fast load times.

• Experience collaborating with Finance, GTM, and Product to define key metrics (like CAC, LTV, or Churn) and codifying them into the data layer.
• Familiarity with data ingestion tools like Fivetran , Airbyte, Workato, Mulesoft to sync data from various SaaS platforms into the warehouse.

• Operates as a full-stack Data Engineer with the technical authority to architect and build entire data lifecycles, from raw source ingestion to high-impact data visualization.
• Familiarity with business-critical systems like Salesforce, Chargbee, Gainsight, and product analytics tools.
• Strong communicator with a track record of working across technical and non-technical teams to drive alignment.
• You have a high appetite for Technology and AI and a natural curiosity for how it can transform your work. You’re comfortable using AI tools to automate repetitive 'busy work,' freeing you up to focus on high-impact work.

We believe AI is a career-defining inflection point. ROLLER is a fast adopter of new technology like AI, and every team member is empowered to own their learning and use the latest tools to supercharge their impact. We’re looking for candidates with the proficiency and curiosity to embrace AI and technology — not just as a technical skill, but as a core competency to help us achieve big goals

Preferred Qualifications

• Experience partnering with IT or Business Technology teams to drive cross-functional outcomes.
• Exposure to customer success and revenue workflows, such as onboarding journeys, renewal metrics, or support analytics.
• Familiarity with AI/ML pipelines and experimentation frameworks that support data-driven innovation.

Perks!

• You'll get to work on a category-leading product that customers love in a fun, high-growth industry! Check our Capterra and G2 reviews.
• ROLLER Recharge days to celebrate and recharge once we've hit our goals
• Engage in our 'Vibe Tribe' - led by our team members; you can contribute to company-wide initiatives directly. Regular events and social activities, fundraising & cause-related campaigns...you name it. We're willing to make it happen!
• Team Member Assistance Program to proactively support our team's health and wellbeing - access to coaching, education modules, weekly webinars, and more.
• 16 weeks paid Parental Leave for primary carers and 4 weeks paid Parental Leave for secondary carers.
• Work with a driven, fun, and switched-on team that likes to raise the bar in all we do!
• Individual learning & development budget plus genuine career growth opportunities as we continue to expand!

What You Can Expect

1. Initial call with our Talent Acquisition Manager
   You'll have an initial call with our Talent Acquisition Manager to chat through some of your experience to date, salary expectations and you can check off any initial questions you might have.
2. Interview with the Hiring Manager
   You'll get to meet with the hiring manager to learn more about the role & ROLLER whilst also talking through your experience in more detail.
3. Loop Interviews
   This is where you will get to meet our wider ROLLER team to do a 'vibe check' on us to make sure our culture & vibe meet what you are looking for!
4. Offer
   If all lights are green and the fit feel right, we'll conduct reference checks and you'll receive an offer to join!

The Field Analytics Team

As part of Okta’s Go-To-Market Strategy & Operations group, the Field Analytics team is the primary engine for strategic analysis, enabling our leaders to make evidence-based decisions that determine the trajectory of Okta’s growth.

Manager, Field Analytics Opportunity

We are looking for a Manager, Field Analytics to serve as a strategic partner of our Solutions Engineering and Partner organizations. You will transform complex data into actionable strategies, identifying key revenue drivers and building scalable analytical frameworks that directly influence Okta's strategic direction.

You will act as the primary liaison between our data infrastructure and our most technical go-to-market branches. Your mission is to build scalable, flexible reporting that drives proactive insights that optimize technical sales productivity and accelerate partner-led growth while championing the adoption of emerging AI capabilities. 

Impact & Responsibilities:

• Strategic Partnership: Act as a dedicated analytics lead for the Pre-Sales and Partners operations and leadership teams. Develop deep relationships to understand their business rhythms and operational needs, translating complex business questions into analytical frameworks.
• AI Readiness & Innovation: Lead the "AI readiness" initiative for field data. This includes exploring AI solutions to help pre-sales and partner teams drive growth, document processes, and brainstorm innovative ways to use AI tools for internal productivity.
• Analytics Delivery: Design, build, and maintain high-visibility Tableau dashboards and reporting assets. You will work in close partnership with our offshore delivery team to progress work faster and scale foundation reporting while you focus on high-complexity strategic projects.

Required Skills & Qualifications: 

• 6+ years in an operational and analytical capacity (Sales Operations, Business Intelligence, or Revenue Analytics), preferably within a fast-paced SaaS environment.
• Expert-level proficiency in SQL (Snowflake), BI Visualization tools (Tableau), and CRM data (Salesforce).
• Proven ability to build advanced analytical models (e.g., productivity modeling, variance analysis) and high-visibility dashboards.
• Ability to look beyond the "what" to the "why" and "so what," providing actionable recommendations that change business behavior.
• Exceptional written and verbal communication skills, with a proven ability to distill complex technical findings into strategic narratives for senior executives.
• A relentless curiosity and a desire to "build it and own it," working both independently and collaboratively across a global delivery model.

Preferred Qualifications: 

• Track record of experimenting with AI tools (e.g., Gemini, ChatGPT, Claude Code) or implementing AI-based workflows to optimize data modeling and automate documentation.
• Proficiency with scripting languages (Python) and modern data modeling frameworks (dbt).
• Prior experience supporting a Solutions Engineering or Partner/Channel organization.
• Experience in SaaS GTM.

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#P5992_3420266

As the Senior Manager, Finance Data Governance, you will be the architect of trust for Okta’s Finance organization. In an era where AI is redefining financial velocity, data integrity is our most valuable currency. You will sit at the intersection of cutting-edge AI innovation and rigorous financial compliance, ensuring our data is not only accessible but undeniable.

You will lead the Finance Data Governance program, serving as the bridge between Finance leadership, Enterprise Data Governance, TDI, Privacy, and Legal. You'll administer the Finance Data Governance Council (FDGC), ensuring Finance implements enterprise governance standards while addressing domain specific financial data requirements. Your goal is to transform "data governance" from a compliance checkbox into a competitive advantage that accelerates AI adoption, Analytics, and executive decision-making.

This is a hybrid role with an in-office requirement of two days per week at our San Francisco location.

Key Responsibilities

1. Policy Creation & Strategy

• Provide strategic direction, oversight, and accountability for the Finance organization's data governance efforts.

• Implement enterprise data governance standards within the Finance domain, defining Finance-specific handling requirements, use case guidance, and operational procedures that extend corporate policies. This includes policies for data classification, retention, privacy, plus AI-specific policies (transparency, bias mitigation, and “hallucination” detection).

• Draft specific guidelines for the Ethical Use of AI in Finance, ensuring automated forecasting and risk models are transparent and unbiased.

• Define Clear Ownership: Establish "Data Domains" and assign accountability to Data Stewards within the Finance Org.

• Establish the scope and priorities for governance initiatives (which domains or systems to focus on) based on business criticality / AI readiness

• Identify existing data stewards within Finance and formalize their accountability through clear role definitions and decision rights.

• Align Finance governance practices with enterprise data classification taxonomy and handling standards, adapting enterprise policies for Finance-specific use cases.

• Be the Data cultural change agent for Finance.

2. Administration & Execution

• Operationalize data policies into daily workflows in Finance and upstream at point of entry, ensuring that automated systems and manual entries adhere to established standards.

• Partner with cross functional data owners to drive upstream data ownership and cleanliness.

• Define requirements for data transformation logic (dbt, SQL), access control models, define data quality thresholds, and data contracts.

• Define and govern the Data Lineage Framework for critical financial metrics, establishing the standards for source-to-report traceability (e.g., ensuring 'Revenue' figures are fully auditable to underlying contracts and invoices). Oversee the implementation of these guardrails to ensure end-to-end data integrity across the finance ecosystem.

• Hold data owners and stewards accountable for data health in their functions. Monitor data quality metrics (Accuracy, Completeness, Timeliness) and lead remediation efforts when data falls below threshold.

• Communicate risks by framing governance gaps in business impact terms, escalate compliance risks (SOX, CMMC).

• Define standards and drive adoption through value realization and business impact, not through mandates.

• Manage the Metadata Repository and Business Data Dictionary to ensure "Revenue" or "Churn" means the same thing in every report.

• Partner with TDI product owners for technical implementation of governance controls in Finance systems.

3. Leadership of the Finance Data Governance Council (FDGC)

• Administer the FDGC program: coordinate meeting logistics, own agendas, facilitate discussions, and execute council decisions. The Council itself comprises VP-level Finance leaders who provide strategic direction and resolve escalated issues.

• Conflict Resolution: Facilitate agreement on common data definitions, master data, metadata as needed.

• Roadmap Management: Track and report on the progress of governance initiatives to executive leadership.

• Translate Risk to Value: Convert complex governance gaps and regulatory risks (SOX, GDPR, EU AI Act) into clear business impact narratives for executive leadership.

• Demonstrate Business Impact: Align governance efforts to business outcomes to reinforce the value of governance investment.

4. AI Data Governance & Best Practices

• Ensure data governance requirements are embedded throughout the model development and deployment lifecycles, including data quality validation, provenance tracking, and bias assessment.

• Define Finance’s certification standards and governance protocols for 'Gold' data products. Ensure that governed, pre-validated views serve as the authoritative 'Truth' layer to mitigate hallucination and model risk.

• Regulatory Compliance: Ensure all data practices comply with SOX, GDPR, and emerging AI regulations (like the EU AI Act).

• Best Practice Evangelism: Conduct training sessions to upskill the Finance team on subjects such as the purpose of governance and the FDGC, data literacy, data quality practices, ethics and AI prompts.

Qualifications

• Experience:

• 5+ years in Data Governance, Data Management, or Financial Systems

• 3+ years leadership/management experience

• Finance domain familiarity (FP&A, Controllership, or financial reporting)

• Communication skills to translate technical issues for executives

• Experience with SaaS business metrics (ARR, Churn, Net Retention) and how they flow through systems.

• Proven track record of taking a data environment from ungoverned to standardized. 

• Data engineering/analytics background

• AI/ML governance experience

• CDMP certification

• SOX compliance experience

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(P24844_3393991)

The Global Data & Insights Team

Okta's Enterprise Data & Insights team powers the data infrastructure that drives decision-making across the company by building reliable pipelines, scalable data platforms, and production-grade data products. We partner closely with internal Data and Insights Analysts as well as external Okta Product teams to unlock business value through robust data architecture, efficient data movement, and the engineering foundations that make data trustworthy at scale.    

The Senior Analytics Engineer, Marketing Opportunity

We are seeking a Senior Analytics Engineer to serve as the technical architect of our marketing data engine. You will own our marketing attribution logic and end-to-end funnel performance data, evolving our current processes into an enterprise-grade ecosystem. Your mission is to uncover the high-fidelity signals that drive new customer acquisition and maximize expansion within our existing base. You’ll tackle a sophisticated roadmap of advanced analytics, transforming raw touchpoints into a trusted, scalable framework for growth.

This role sits at the intersection of business context and technical execution. You will design scalable data models, define consistent business logic, and help establish a strong semantic foundation that enables both human analytics and machine-driven intelligence.

You will partner closely with Marketing stakeholders, Data Analysts, Data Scientists, and Data Engineers to ensure data is accurate, consistent, and easy to consume; whether through dashboards, self-service exploration, or AI-powered workflows.

What you’ll be doing

Data Modeling & Semantics

• Design, build, and maintain scalable data models using dbt and Snowflake
• Define and standardize core Marketing metrics (e.g., pipeline, campaign performance, attribution, conversion rates)
• Establish consistent modeling patterns, naming conventions, and semantic clarity across datasets
• Contribute to a shared semantic layer that enables consistent reporting across channels and tools

AI-Ready Data & Snowflake Ecosystem

• Prepare high-quality, well-governed datasets for use with Snowflake Cortex and Snowflake Intelligence
• Enable data foundations that support LLM-powered use cases such as campaign insights, segmentation, and performance optimization
• Ensure data is context-rich, well-documented, and aligned with business meaning to improve AI accuracy and usability

Data Quality, Governance & Trust

• Implement robust testing, validation, and documentation practices in dbt
• Ensure consistency across dashboards and marketing tools through shared definitions and reusable models
• Apply data governance best practices, including access controls, lineage, and auditability
• Partner across teams to establish clear ownership of marketing data assets and definitions

Collaboration & Delivery

• Partner with Marketing, Customer First, and Analytics teams to translate business needs into scalable data solutions
• Support self-service analytics by building intuitive, reusable datasets across channels (e.g., digital, lifecycle, events)
• Contribute to scalable data workflows that balance immediate campaign needs with long-term maintainability
• Work within an agile environment, contributing to planning, prioritization, and continuous improvement

AI and Data Mindset

• Demonstrate an AI-first mindset, thinking beyond data models and dashboards to how data can power intelligent systems and decision-making
• Understand the importance of well-modeled, well-documented, and semantically clear data for AI and LLM-based use cases
• A level of comfort leveraging AI-assisted workflows to improve productivity, code quality, and consistency
• Curiosity for emerging capabilities in platforms like Snowflake Cortex and Snowflake Intelligence, and how they can be applied to Marketing analytics

What you’ll bring to the role

• 5–8+ years of experience in Analytics Engineering, Data Engineering, or similar roles
• Strong SQL skills and experience building analytics-ready data models
• Mentorship & Engineering Excellence: Mentorship, raising the technical bar, establishing organization-wide standards for dbt/SQL quality and CI/CD
• Hands-on experience with dbt and Snowflake or other ETL, Modeling and database platforms
• Solid understanding of data modeling principles, including dimensional modeling and semantic design
• Experience working with Marketing data (e.g., campaign data, web analytics, CRM, attribution models)
• Familiarity with tools such as Salesforce, Marketo, Google Analytics, or similar platforms
• Experience with data quality, testing, and documentation best practices
• Exposure to Python, R, or data processing frameworks (e.g., PySpark) is a plus
• Experience with BI tools such as Tableau or Looker
• Strong communication skills and ability to work across technical and business teams

What you can look forward to as an Okta employee!

• Amazing Benefits
• Making Social Impact
• Fostering Diversity, Equity, Inclusion and Belonging at Okta 
• Okta cultivates a dynamic work environment, providing the best tools, technology and benefits to empower our employees to work productively in a setting that best and uniquely suits their needs. Each organization is unique in the degree of flexibility and mobility in which they work so that all employees are enabled to be their most creative and successful versions of themselves, regardless of where they live. Find your place at Okta today! https://www.okta.com/company/careers/.

Okta is an Equal Opportunity Employer/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, marital status, age, physical or mental disability, or status as a protected veteran. We also consider for employment qualified applicants with arrest and convictions records, consistent with applicable laws. If reasonable accommodation is needed to participate in the job application or interview process, please use this Form to request an accommodation.

Okta is committed to complying with applicable data privacy and security laws and regulations. For more information, please see our Privacy Policy at https://www.okta.com/privacy-policy/. 

Each organization is unique in the degree of flexibility and mobility in which they work so that all employees are enabled to be their most creative and successful versions of themselves, regardless of where they live. Find your place at Okta today! https://www.okta.com/company/careers/.

Okta is an Equal Opportunity Employer/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, marital status, age, physical or mental disability, or status as a protected veteran. We also consider for employment qualified applicants with arrest and convictions records, consistent with applicable laws. If reasonable accommodation is needed to participate in the job application or interview process, please use this Form to request an accommodation.

Okta is committed to complying with applicable data privacy and security laws and regulations. For more information, please see our Privacy Policy at https://www.okta.com/privacy-policy/.

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P24712_3413650

P24715_3413656

Discord is looking for an experienced and passionate Senior Data Engineer, Analytics to join our data team! You will be owning the transformation and semantic layer that turns data into clean, tested, well-documented tables and dashboards that data scientists, product managers, and business stakeholders can trust and self-serve from. You'll define and operationalize the metrics that inform how we identify opportunities, measure success, and make decisions. If this sounds exciting to you and you’re passionate about data modeling, metric design, and empowering teams to move faster with reliable data, read on!

We're hiring for multiple verticals on our Data Engineering team, including Engagement and Performance.

What you will be doing:

• Design, build, and maintain curated analytical datasets and data models that serve as canonical sources for metrics, dashboards, and analyses
• Own metric definitions end to end, from partnering with data science and product team to define what we should measure, to implementing it in production, to surfacing it in visualization tools
• Build and maintain executive-level dashboards and self-serve reporting tools that enable business stakeholders to answer their own questions
• Partner closely with data science, product manager, and engineering teams to translate business questions into well modeled, performant, and discoverable data assets
• Establish and enforce data quality standards through testing frameworks, documentation, and monitoring for the datasets you own
• Drive adoption of consistent data modeling patterns, naming conventions, and documentation norms across the data organization

What you should have

• 4+ years of experience in analytics or data engineering with a strong focus on building curated, consumer-facing datasets
• 4+ years of experience in designing, developing, and maintaining robust data models from structured and unstructured sources
• 4+ years of experience writing accurate and effective code in SQL
• Experience with designing data model to power a variety of use cases, including experimentation
• Fluency in Python or another programming language, with solid fundamentals in version control and CI/CD practices
• Experience building and owning executive-level dashboards and reports using BI tools (e.g., Looker, Tableau, or similar)
• Strong business acumen, you will partner with data scientists and product managers to translate ambiguous business questions into concrete metric definitions and data models
• Excellent communication, comfortable being the connective tissue between technical and business teams
• Last but not least, desire to work with amazing, passionate people who care deeply about solving challenging problems to improve Discord

Bonus Points:

• Passion for Discord or online communities
• Experience building or contributing to a semantic layer or metrics store
• Experience with modern analytics and data engineering tools and workflows (dbt, BigQuery, or similar)
• Experience defining and governing metric standards across a data organization
• Experience working on analytics for core features (i.e. messaging, video/voice call etc) in communication app.
• Experience working on analytics for performance and reliability of communication app
• Background in analytics for core communication features (messaging, video/voice) or platform reliability and performance.

The US base salary range for this full-time position is $196,000 to $220,500 + equity + benefits. Our salary ranges are determined by role and level. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include equity, or benefits.

Discord is looking for an experienced and passionate Data Engineer, Analytics to join our data team! You will be owning the transformation and semantic layer that turns data into clean, tested, well-documented tables and dashboards that data scientists, product managers, and business stakeholders can trust and self-serve from. You'll define and operationalize the metrics that inform how we identify opportunities, measure success, and make decisions. If this sounds exciting to you and you’re passionate about data modeling, metric design, and empowering teams to move faster with reliable data, read on!

We're hiring for multiple verticals on our Data Engineering team, including Growth and Safety.

What you will be doing:

• Design, build, and maintain curated analytical datasets and data models that serve as the canonical sources for metrics, dashboards, and analyses.
• Partner closely with data science, product managers, and engineering teams to translate business questions into well-modeled, performant, and discoverable data assets.
• Execute metric workflows, from metric definition and logging schema design to data modeling and visualization, with guidance from manager and senior team members.
• Build and maintain executive-level dashboards and self-serve reporting tools that enable business stakeholders to answer their own questions.

What you should have

• 2+ years of experience in analytics or data engineering with a strong focus on building curated, consumer-facing datasets
• 2+ years of experience in designing, developing, and maintaining robust data models from structured and unstructured sources to power a variety of use cases, including experimentation
• 2+ years of experience writing accurate and effective SQL
• Fluency in Python or another programming language
• Experience building and owning executive-level dashboards and reports using BI tools (e.g., Looker, Tableau, or similar)
• Strong business acumen, you will partner with data scientists and product managers to translate ambiguous questions into concrete metric definitions and data models
• Excellent communication, comfortable being the connective tissue between technical and business teams
• Last but not least, desire to work with amazing, passionate people who care deeply about solving challenging problems to improve Discord

Bonus Points:

• Passion for Discord or online communities
• Experience building or contributing to a semantic layer or metrics store
• Experience with modern analytics and data engineering tools (dbt, BigQuery etc)
• Experience implementing and monitoring audits for data quality with massive data sets (e.g. billions of rows)
• Experience working on SEO, GEO, or other top of funnel growth focused features.
• Experience collaborating with compliance, legal, or litigation cross-functional teams.

The US base salary range for this full-time position is $160,000 to $180,000 + equity + benefits. Our salary ranges are determined by role and level. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include equity, or benefits.

About the team/role

We’re hiring an ML Engineer (P2) to build and ship the AI systems that power Checkr’s core products. This role sits on the ML team inside Checkr’s Data & ML organization within Engineering.

Checkr runs millions of background checks a year. The ML team builds the systems that make those checks faster, more accurate, and cheaper to operate: document processing, charge classification, entity resolution, and in-product intelligence. These are production services that Product Engineering depends on daily.

This is not a research role or a notebook role. You’ll own ML services end-to-end: design them, code them, deploy them, monitor them. We need someone who writes production software, builds with LLMs and APIs as first-class tools, and can tell the difference between working code and AI slop. If you’ve spent the last few years building AI-native software and you care deeply about engineering craft, we want to talk.

This role sits in the central Data & ML team within the Engineering organization. You will partner daily with Product Engineering, Product, and cross-functional teams. You’ll also contribute to Checkr’s broader AI strategy, including our initiative to deploy our agentic fleet and build scalable context with our semantic layer.

We are looking for someone based in San Francisco who has built ML systems in fast-moving, impact-first environments. Less process, more shipping. Less paperwork, more results.

What you’ll do

• Build and deploy ML/AI services. Design, develop, and ship ML models and AI systems that Product Engineering teams rely on. You write the model code, the API layer, the monitoring, and the tests. Not notebooks; production services.
• Design with LLMs and APIs. Use LLM APIs (OpenAI, Anthropic, etc.) as building blocks in production systems. You know when to call an LLM, when to fine-tune, when to use a classical model, and when to write a rule. You think about cost, latency, and quality together.
• Ship production software. Write clean, well-structured code with solid OOP, proper abstractions, error handling, and tests. Your code gets reviewed by SWEs and passes. CI/CD is how you work, not something you bolt on at the end.
• Partner with product and engineering. Translate business problems into ML solutions. Define API contracts with product engineers. Explain your approach clearly to non-ML partners and leave the room with alignment, not confusion.
• Evaluate and iterate fast. Build evaluation frameworks, run experiments, and make data-driven decisions about model and system performance. Ship and iterate; don’t wait for perfect.
• Ship AI-powered workflows. Put AI to work on your own processes: automate pipelines, build agentic workflows, and contribute reusable skills and context to Checkr’s agentic platform. The expectation is that our teams operate AI-first.

What you bring

• A Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field, or equivalent depth from experience
• 4+ years building software professionally, with at least 2 of those building ML systems that run in production
• Strong Python fluency; you write clean, testable, well-structured code with solid OOP instincts. Not scripts; software
• Hands-on experience using LLM APIs in production systems: prompt engineering, structured outputs, function calling, cost management, and evaluation
• You’ve built and maintained APIs, worked with CI/CD pipelines, and shipped code that other engineers depend on
• Comfortable with distributed systems concepts: queues, async processing, caching, horizontal scaling
• Experience with NLP tasks in production: classification, extraction, entity resolution, summarization
• Comfort with and enthusiasm for AI-assisted workflows; experience using LLMs, code-generation tools, or agentic systems in production or operational contexts is a strong signal
• You can evaluate tradeoffs: fine-tune vs. prompt, hosted vs. self-deployed, classical ML vs. LLM, rule vs. model
• Strong communication skills; you explain technical decisions clearly to engineers and non-engineers alike, without hiding behind jargon
• You use AI tools (Copilot, Claude, etc.) to move faster, but you understand every line they produce. You can spot AI slop and you don’t ship it
• An A-player mindset with a strong bias for action: you raise the bar, move with urgency, stay resilient through ambiguity, and take ownership to deliver meaningful outcomes.

Nice to have

• Experience with MLOps platforms (MLflow, SageMaker, Vertex, or similar)
• Background in document processing, OCR, or information extraction
• Experience with PySpark or large-scale data processing
• Ruby experience (Checkr’s platform runs on Rails)
• Familiarity with compliance-sensitive domains (fintech, legal tech, HR tech)
• Working knowledge of dbt, Snowflake, or modern ELT/data transformation tools

What you’ll get

• A fast-paced, low-bureaucracy environment where shipping matters more than process
• Direct exposure to executive and C-level decision-making
• A seat on the Data & ML team shaping Checkr’s AI strategy
• Learning and development allowance
• Competitive cash and equity compensation and opportunity for advancement
• 100% medical, dental, and vision coverage
• Up to $25K reimbursement for fertility, adoption, and parental planning services
• Flexible PTO policy
• Monthly wellness stipend

About the Role

The Revenue Operations team at Gusto is a full-stack team responsible for data, analytics, and operational excellence in pursuit of scaling our revenue growth. The Go-To-Market Analytics team is one functional area, responsible for defining and owning the metrics for scaling our Sales teams. 

Gusto is looking for a Revenue Analytics Engineer to build and maintain foundational data infrastructure that is crucial to growing and scaling our Sales efforts. In this role, you will partner with our GTM analysts to scope and deliver data products for an audience ranging from company and revenue leadership to operational and frontline sales. In doing so, you will own significant portions of our sales data from end-to-end, focusing on the transformation, design, and visualization workflows. In addition, you will establish best practices for systems design and workflows to maximize usefulness of AI at scale. 

This role will report to the Head of Go-To-Market analytics, and partner closely with Data Science, Data Platform, and Business Technology teams. 

Here’s what you’ll do day-to-day:

• Establish relationships with internal stakeholders to determine business needs, scoping and delivering solutions through data products
• Design, build, and maintain data pipelines and dashboards to automate “keep the lights on” work out of manually-managed processes
• Build a data foundation by incorporating business logic into raw data tables, laying the groundwork to enable value-add insights
• Create a dynamic reporting environment based on stakeholder needs
• Identify data discrepancies and set best practices for data logic to create a clean and well-documented environment
• Collaborate with broader data organization as steward to the definition and process management of core Gusto metrics

Here’s what we're looking for:

• Education or work experience in Engineering or Computer Science, or a related technical field 
• 7+ years of experience in an analytics engineering, business intelligence, or technical data analytics role
• Experience with SQL and ETL optimization techniques, especially within cloud-based data warehouses like Redshift, Snowflake, etc.
• Command-line experience and familiarity with version control collaboration tools (git)
• Experience with data pipeline management technologies with dependency checking, such as Airflow, as well as schema design and data modeling tools (dbt)
• Experience with data visualization technologies, e.g., Tableau, Looker, Sigma, Mode, Hex
• Experience with Python and data ingestion tools
• Ability to problem-solve open-ended problems and project manage dependencies and timelines to optimal outcomes
• Ability to demonstrate tools, business intuition, and attention to detail for data validation and QA 

Our cash compensation amount for this role is targeted at $138,000-156,000 in Denver & most remote locations, and $168,000-189,000 in New York & San Francisco Bay Area. Stock equity is additional. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.

The Data Engineering team builds tools and systems that make Gusto's data consistent, user-friendly, and helpful. This helps our teams make decisions using data and provide intelligent, customized experiences to our customers.

We're looking for a Senior Data Engineer who can take loosely defined problems and drive them end-to-end—from framing the problem and aligning stakeholders to designing, building, and delivering durable data solutions. You’ll partner closely with analytics, product, and engineering teams to deliver data solutions that drive real business and customer impact.

Preferred Qualifications

• 8-10+ years of industry experience in data engineering building scalable data pipelines and data products
• Strong proficiency in SQL and at least one programming language (e.g., Python, Scala, or Java)
• Proven experience building and maintaining robust data pipelines and ETL workflows, with hands-on dbt experience for reliable, testable, and maintainable data transformations.
• Hands-on experience ingesting data from diverse sources, including APIs, databases, SaaS applications, and event streams.
• Strong foundation in data modeling, schema design, and data quality best practices, with functional experience working on cloud platforms like Snowflake, Redshift, BigQuery, or Databricks.
• Experience implementing CI/CD pipelines, automated testing, and data observability to ensure reliability and trust in data systems
• Familiarity with monitoring, alerting, and incident response for production-grade data pipelines
• Proven ability to optimize performance and cost across data workflows and storage systems
• Functional understanding of how to leverage AI and automation in data engineering — building self-service tools, intelligent pipelines, and agents that automate repetitive tasks
• Exemplar problem-solving skills and ability to work collaboratively in cross-functional teams.
• Strong communication and collaboration skills, with a focus on clarity, empathy, and shared ownership

Compensation Details

Our cash compensation amount for this role is targeted at $155,000-$185,000/yr in Denver, $170,000-$200,000/yr in Los Angeles, and $190,000-$220,000/yr for San Francisco, Seattle, and New York. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed above.

The Opportunity:

Grow Therapy is seeking a Staff Data Analyst to drive data-informed decision-making across our Match Success product group. In this role, you will lead analytical initiatives that help us understand how patients and therapists connect and begin their journey together, and shape the product experiences that make therapy accessible and effective. You will partner closely with Product and cross-functional stakeholders to set the analytical agenda, own business-critical reporting, use data to improve products, and run rigorous experiments, while helping raise the bar for how data is used across the team.

What You’ll Be Doing:

• Partner with Product and other technical stakeholders to prioritize analytical needs and support product launches, decision-making, and ad hoc requests through clear, data-driven insights.
• Own analytical strategy and define the metrics, reporting frameworks, and experimentation roadmap that guide product direction and OKR development for the team
• Design, maintain, and scale business-critical dashboards and datasets that give stakeholders reliable, self-serve access to the metrics that matter.
• Lead experimentation end-to-end, from thoughtful test design through execution, analysis, and clear, actionable recommendations that drive product decisions.
• Craft compelling data narratives and visualizations that translate complex findings into crisp takeaways for product, leadership, and executive audiences.
• Collaborate with data scientists and data engineers to improve logging, pipeline reliability, data availability, and modeling foundations (including dbt).
• Partner with other analysts, contribute to shared tooling and frameworks, and help set standards for analytical rigor across the team.

You’ll Be a Good Fit If: 

• You have 7+ years of experience applying analytics to complex, cross-functional business problems in a high-growth, product-focused environment.
• You are a self-directed owner who proactively identifies high-impact opportunities, navigates ambiguity well, and drives work forward without waiting to be asked.
• You are deeply fluent in SQL and have strong working knowledge of experimental design, including A/B testing methodology and statistical interpretation.
• You have a proven track record building business-critical dashboards and data visualizations, ideally in Hex, Looker, or similar platforms.
• You have experience with data modeling (dbt) or a strong track record of quickly picking up new technical tools and frameworks.
• You are a skilled data storyteller who knows how to extract insight from complexity and communicate it clearly to both technical and executive audiences.
• You build strong relationships across functions, comfortable partnering with engineers, PMs, and executives, and you know how to earn trust through rigor and clear communication.
• You care deeply about expanding access to mental health care and are motivated by work that has a direct impact on patient and provider outcomes.`
• Experience with advanced analytics techniques such as python, ML models, LLM, etc., is a plus.

Role Details:

• Employment Type: Full-Time, Exempt
• Base Compensation: The base compensation range for this position is
       Hybrid Commitment: $182,000 - $227,000 USD Annually
    

This role is hybrid (onsite from our San Francisco hub location three days per week: Tuesday, Wednesday, Thursday) This include travel 2–3 times per year (e.g., company and department offsites).

The base compensation for this role will vary depending on several factors, including relevant experience, qualifications, and the candidate’s working location.

The Opportunity:

We are looking for a Data Analyst to help us drive data-driven insights and reporting in Product at Grow Therapy, and who will work on a variety of reporting, analytics and experimentation projects. You’ll join a broader data organization of analysts, data scientists, and data engineers, and be part of a San Francisco–based analytics team reporting into an Analytics Manager. This role will support our Lives Covered product group. 

What You’ll Be Doing:

• Partner with Product and other technical stakeholders to prioritize analytical needs and support product launches, decision-making, and ad hoc requests through clear, data-driven insights
• Develop, maintain, and evolve core product and performance reporting, including business-critical dashboards built on scalable, reliable datasets
• Support experimentation and A/B testing through thoughtful design, execution, analysis, and clear, actionable conclusions
• Create compelling data visualizations and storytelling assets to effectively communicate insights to diverse audiences
• Collaborate with senior and staff analysts on complex, high-impact analytical projects, including data modeling and OKR development
• Work closely with data scientists and data engineers to improve data availability, integrity, accuracy, reliability, and logging/pipeline quality

You’ll Be a Good Fit If: 

• You have 3+ years proven experience in using analytics to tackle business problems that cross multiple product/project areas and teams, ideally in a high-growth, fast-paced environment.
• You have strong SQL skills and intermediate understanding of experimental design (such as A/B experiments).
• You are a wizard at building business critical dashboards/data visualizations (ideally within Hex or Looker, with strong LookML expertise)
• You have experience with data modeling (dbt) or are eager to learn
• You love data storytelling, creating a compelling narrative by extracting insights from data, and summarizing learnings / takeaways for executives.
• You enjoy communicating effectively and building relationships with both technical and non-technical stakeholders.
• You deeply value the importance of expanding access to mental health care.
  
  

Employment Type: Full Time, Exempt 
Base Compensation: The base compensation range for this position is $124,000–$145,800 USD Annually.

This is a hybrid role with the expectation to work onsite from our San Francisco hub location three days per week (Tuesday, Wednesday, and Thursday) and travel 2–3 times per year (e.g., company and department offsites).

The base compensation for this role will vary depending on several factors, including relevant experience, qualifications, and the candidate’s working location.

The Role

Analytics Engineering at Flex sits at the intersection of Data, Product, Engineering, and business objectives. As Director, Analytics Engineering you will run the team that enables stakeholders at Flex to easily find the data they need to make decisions. You’ll be responsible for developing a world-class analytics platform that powers all data consumption at Flex, as well as managing and developing a team of high-performing Analytics Engineers. You'll also be hands-on, leveraging your technical skills to develop data models and build better data products and solutions. You'll partner with analysts, engineers, PMs and others to define the requirements while being accountable for delivering the end data product.

You Will:

• Develop and lead strategy for data modeling platform and team roadmap
• Manage and mentor a team of Analytics Engineers
• Collaborate with leaders across product & engineering teams to define data requirements and inform architecture/schema design decisions
• Lead the development of data models to consume, transform, and expose data to stakeholders and systems across the company
• Collect business requirements, define successful analytics outcomes, and design data solutions
• Set company-wide standards for data regarding structure, quality, and expectations
• Contribute Flex’s data culture by influencing processes, systems, and tools that enable to make better decisions in a scalable way

You Have:

• 8+ years as an analytics engineer or equivalent, with a track record of shipping high quality products and features at scale
• Expert knowledge of SQL, data warehouses (we use Snowflake), and how to write efficient SQL queries
• Familiarity with common BI tools (preferably Sigma Computing, Tableau, Mode, etc.)
• Expert proficiency with dbt, dbt Cloud, and Airflow
• Strong perspective on analytics engineering development cycle (data modeling, version control, documentation + testing, and best practices for codebase development and CI/CD)
• Proficiency with Python preferred
• Experience within the fintech space preferred

Flex takes a market-based approach to pay, and compensation may vary depending on your primary work location. Work locations are categorized into one of three tiers based on a cost of labor index for that geographic area.  

Tier A (NYC/SF/Seattle): $240,000-290,000
Tier B (Austin, Washington D.C. Philadelphia, Los Angeles, San Diego, Chicago, Miami, Atlanta : $215,000-265,000
Tier C (SLC/All Other States): $200,000-250,000

Offices

Roles posted in New York, San Francisco, or Salt Lake City are hybrid positions with on-site expectations of 2-3 days per week in our local offices. For candidates outside of these areas, you may be eligible for our relocation assistance program.

As one of our Senior Data Analysts of our Global Data team, you will work closely with the expense team and stakeholders from across the business including engineers, sales, marketing, finance, and operations to define the data needs and products which will drive the business forward. The ideal candidate will be extremely curious, independent, knowledgeable about data, and will use their data skills and business mindset to make a difference every day. We are looking for people who can operate at a company that grows as fast as ours by dealing with multiple moving pieces while still holding up quality, long-term thinking, and delivering value to our customers. We take great pride in our diversity of backgrounds, cultures, and perspectives and we strongly believe this is helping us to grow as a successful and impactful team. 

What You'll Do

• Identify trends and opportunities for growth through analysis of complex data sets
• Work alongside stakeholders (from ICs/Engineers to Directors/E-Staff) to understand requests and provide solutions using data analytics
• Provide data solutions end-to-end (working with raw data, developing SQL models, building dashboards and presenting insights to stakeholders)
• Support and strengthen our fast paced fintech product team in all functional areas and special requests as needed
• Create and deliver documentation and training on reporting and BI tools for business users to enable self-service on company data
• Consolidate data across business units and acquisitions with different definitions and measurement. 
• Ongoing commitment to identify and implement process improvements for operational efficiencies and enhanced analytics as well as maintain existing documentation of control procedures performed by the financial planning and analysis team
• Develop SQL models using data warehousing principles (we use dbt).

What We're Looking For

• Preferred Bachelor’s degree or equivalent experience
• Over 5 years of professional experience in the field of Data Analysis
• Proficiency with Data Visualization Tools for at least 2 years
• Strong command of SQL
• Prior experience in fintech is a big plus
• Ability to effectively interpret business requirements and translate them into analytical solutions, insights, and reports
• Proficient in standard statistical techniques such as significance testing and regression modeling
• Demonstrated eagerness to acquire new technological skills and problem-solving abilities
• Familiarity with Snowflake and dbt is beneficial
• Excellent communication skills and adept at building relationships with stakeholders; must also be a collaborative team player

We're hiring a RevOps Manager to own variable pay and quota-setting across our GTM organization, automate the majority of this cycle, and support/automate other RevOps functions - from analytics and forecasting to process improvement and tooling. 

Who You Are

Required:

• 3+ years in revenue operations, sales operations, sales compensation,  finance, or a closely adjacent function, with hands-on ownership of sales comp work
• Daily working fluency with AI tools (Claude, Cursor, ChatGPT, n8n, Replit, or similar)
• At least one shipped agentic workflow, internal tool, or skill that took real work off your plate or someone else's, with specifics on what it does and what it saves
• Comfort directing AI to write SQL and validate outputs against source data. Independent SQL fluency is a plus
• Cross-functional working style with Finance, Legal, and Sales leadership
• Judgment under pressure during compressed close windows

Nice to Have:

• Experience designing or improving quota-setting methodologies
• Audit support experience, with a habit of flagging issues in comp data, plan mechanics, or payouts before they escalate
• Familiarity with usage-based comp plans and the attribution challenges they create
• Experience with commission management platforms 
• Exposure to Salesforce data model, dbt, and BI tooling (Mode, Looker)
• Experience leading or co-leading an annual comp plan redesign

What You'll Do

AI & Agentic Automation

• Translate the existing SQL-and-spreadsheets comp process into agents, skills, and workflows so each cycle takes less manual work than the last.
• Examples we'd want to see from you in your first year: a payout-audit agent that pre-flags discrepancies against source data, a methodology skill that drafts and pressure-tests plan changes, an n8n workflow that routes rep escalations to the right reviewer, a Replit app that lets sales managers self-serve crediting questions, an agent that builds rep statements and explains the math.
• Tools we use today: Claude, Cursor, ChatGPT, n8n, Replit. You don't need a software engineering background. You do need a track record of shipping automations that took real work off someone's plate.

Comp Operations

• Run monthly and quarterly comp cycles for all GTM teams. Resolve discrepancies, reconcile with Finance and Payroll, run close QA. On-time cycles, errors caught before they escalate.
• Own the quarterly target-setting pipeline. Collect inputs, generate targets, drive approvals with the VP of Sales and CFO, finalize and distribute target packages. Partner with analytics on dashboards that make targets transparent to managers and reps.
• Handle new-hire guarantees, crediting exceptions, split credit decisions, LOA and territory moves, SPIFF design and payout, and manager-level comp questions. Sales and Finance judge the function on these calls.

Forecasting & Pipeline Analytics

• Own weekly and monthly forecast roll-ups across our GTM segments. Pressure-test rep and manager calls against pipeline data. Surface gaps to the VP of Sales and CFO before they show up in QBRs.
• Build and maintain pipeline dashboards covering coverage, stage conversion, velocity, and aging. Tie metrics back to compensation so managers see the full picture.
• Partner with FP&A on bookings forecasts, attainment modeling, and capacity planning for the next fiscal year.

Plan Design & Methodology

• Lead the annual comp planning cycle. Frame the plan, construct it across teams, run review and approval cycles with the CFO, CEO, VP of Sales, and other leadership. Manage Legal review and acknowledgment. Run the rollout. Own the retrospective.
• Bring a point of view on how plans are designed, how targets are set, and how the process runs. Identify changes that improve fairness or speed, then ship them in the next cycle.

What Success Looks Like

• First 90 days: Complete monthly comp cycles, partner on a quarterly cycle and one target-setting cycle. Know every plan and the people who own them. Ship your first agent or workflow that takes a piece of the cycle off the team's plate.
• First 6 months: Own a quarter-close from source data through payout. Propose specific changes to the operating process, backed by hours saved from the automations you've shipped. Take on forecasting & pipeline analytics work and partner on your first forecasting cycles
• First 12 months: Lead the annual plan design & capacity planning process. Show a portfolio of automations that save time in every subsequent cycle. GTM and Finance leaders bring comp questions to you first.

For CA-based applicants:

The base salary for this position is $130,000 - $170,000.

This position is eligible for equity in the form of stock options.

This position is eligible for Instawork benefits, including:

• Medical, dental, and vision plans with coverage beginning on the date of hire
• Flexible paid time off
• At least 8 paid company holidays annually
• Phone stipend
• Commuter stipend
• Supplemental pay on qualified leaves
• Employee health savings accounts (HSA) contribution
• Flexible spending plans
• 401K plan
• Perkspot - discount program through Lumity

A variety of factors are considered when determining someone's compensation, including a candidate's professional background, experience, and location. Final offer amounts may vary from the amounts listed above.

We are looking for a Staff Security Software Engineer who is passionate about detecting and mitigating abuse in the Cloud. 

As a Staff Security Software Engineer at DigitalOcean, you will join a dynamic team dedicated to revolutionizing cloud computing and AI. This role reports to the Senior Manager of Security Engineering within the Security organization. You should have experience developing and maintaining software systems in a complex, high-scale environment, analyzing outcomes and communicating insights to stakeholders, and rapidly devising solutions that are in alignment with overall business goals. Our team is focused on enabling DigitalOcean to scale safely by building systems that detect and prevent abuse on our platform. We highly value collaboration and growth, and work closely with other teams in the Security organization to deliver high quality solutions and also build tooling to boost their efficiency.

Our customers trust us with their data and operations, and we take that responsibility seriously. Security at DO means solving highly complex problems on a large scale that have real impact for our customers, our products, and for the larger internet community.

What You’ll Do:

• Design, develop, and deploy resilient services and tooling that provide security capabilities at scale, primarily in Go, with some work in Javascript and Python.
• Research and design automated approaches to detecting and actioning abuse and misuse of DigitalOcean products and services.
• Work with product and engineering teams to design and implement secure architectures.
• Design and build internal tools that enable our Security Operations Center to do more, faster.
• Measure the efficacy of our services and tools, using logs, metrics, and audit records, to drive reporting and decision-making, leading to iterative and transformative security improvements as DigitalOcean scales. All while being mindful that security should be an efficiency enabler for the business, not an obstacle.

What You’ll Add to DigitalOcean:

• 8+ years of hands-on experience in software engineering projects, ideally with a security focus, in a complex, high-scale SaaS or IaaS environment. You are an expert in writing robust code with good test coverage and demonstrated success in delivering projects.
• Understanding of cloud-native services and infrastructure provisioning (e.g. compute, storage, networking).
• Deep familiarity with cloud services & infrastructure—compute, storage, network, managed services—and related abuse tactics, including DDoS, spamming, phishing, cryptomining, and bot networks.
• Strong analytical and reporting skills, with proficiency in SQL and data warehouse querying (e.g., Snowflake, Redshift, BigQuery)
• A customer-first mindset—you aim to maximize abuse prevention while avoiding false-positives that could impact high-value customers.
• Excellent communication and documentation skills; you can clearly articulate patterns, mitigation strategies, and tradeoffs to both technical and non-technical stakeholders. You have a record of partnering with internal engineering teams and non-technical stakeholders to gather requirements and tackle security problems across an entire stack with empathy and creativity.
• High integrity, strong judgment, and a track record of working independently on high-impact investigations and policy decisions.

Bonus qualifications:

• Experience with microservice architectures, asynchronous and event-driven processing, and synchronous gRPC/HTTP-based requests.
• Experience in building secure by default services and applications.
• Familiarity with technologies such as gRPC, Docker, Elasticsearch, Prometheus/VictoriaMetrics, Kubernetes, and GitHub Actions.
• Experience with Looker, Snowflake, dbt, and Airflow

Compensation Range: 

• $199,680 - $249,600

*This is a remote role

#LI-Remote

We are building an AI-native Data Cloud and need a VP of Engineering who owns architecture, execution, and business outcomes end-to-end. The buck stops with this role for platform reliability, developer experience, and Data Cloud adoption.

DigitalOcean’s Data Cloud will power AI applications end-to-end—from data ingestion to real-time inference—enabling developers to build production-grade RAG systems, AI agents, and analytics workflows on a single, integrated platform. This role is responsible for delivering that platform by leveraging and extending DigitalOcean’s existing infrastructure, PaaS, and AI capabilities.

This is not a traditional data platform role. You will define and execute the strategy to move DigitalOcean up the stack—from infrastructure provider to a differentiated AI application and inference platform. Success will be measured through roadmap velocity, system reliability, developer adoption, and measurable business impact.

We are looking for a leader who translates strategy into shipped systems, with clear accountability for outcomes.

What You’ll Do:

Data Cloud Platform

Architecture and delivery of a unified platform spanning:

• Ingestion (connectors, CDC, streaming)
• Storage (object, OLAP, vector, graph)
• Processing (ELT pipelines, real-time and batch)
• Query & analytics (SQL, hybrid search, RAG)
• Governance (catalog, lineage, versioning, access control)

AI-Native Capabilities

• Knowledgebase-as-a-Service (RAG pipelines)
• Vector and hybrid retrieval systems
• Feature and embedding pipelines
• Real-time inference data paths

Product & Execution

• Deliver GA-grade services with strong SLAs, cost efficiency, and simplicity
• Drive roadmap aligned to Digital Native Enterprise (DNE) customers
• Establish and manage clear metrics: adoption, retention, ARPU, performance, reliability

Organizational Leadership

• Build and scale a high-performing global organization (US and India)
• Implement single-threaded ownership (STO) across services
• Partner deeply with Product, GTM, and Platform Engineering

What Success Looks Like (12 months)

• Launch a cohesive Data Cloud platform—not a collection of fragmented services
• Achieve meaningful adoption across AI-native workloads (RAG, inference, analytics)
• Reduce developer time-to-value from weeks to hours
• Establish clear differentiation versus hyperscalers on simplicity and cost

What You’ll Bring:

• Proven track record of building and operating cloud-scale data platforms (OLAP, lakehouse, streaming, or ML systems), with strong architectural judgment across storage, processing, and query layers
• Experience integrating open-source ecosystems (e.g., Spark, Airflow, dbt) into managed cloud services at scale
• Experience with AI/ML data pipelines (feature stores, embeddings, RAG)
• Demonstrated ability to operate in ambiguity, make high-quality decisions with incomplete data, and drive multi-quarter execution with measurable outcomes
• Proven experience scaling engineering organizations and delivering under real-world constraints

Critical requirement:
You have personally driven complex distributed systems from concept to production—not just managed teams that did.

Why This Role Matters

The Data Cloud is the control plane for AI applications. If DigitalOcean gets this right, it becomes the platform where developers build and scale intelligent applications—not just where infrastructure runs. If not, it remains an interchangeable infrastructure.

Compensation Range: 

• $279,200.00 - $349,000

*This is a remote role

JR: 2026-7683

#LI-Remote

fal is the generative media ecosystem powering the next generation of AI products. We build the infrastructure, tools, and model access that teams need to move from idea to production, and do it at scale without compromise. For developers and enterprises, fal is the foundation that makes generative media not just possible, but practical: a unified platform where high-performance inference, orchestration, and observability come together to unlock new categories of AI-native products.

As generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on.

About this role:

As a Senior Data Scientist for Product Intelligence, you will architect the analytical foundation for how fal builds and grows. Your domain spans the entirety of the developer journey—from initial acquisition to long-term expansion. You will turn raw behavioral and system signals into the metrics and insights that shape our product roadmap and go-to-market strategy.

This is a high-influence role where you will act as a strategic partner to Product, Engineering, and Marketing. Beyond domain-specific analysis, you will share ownership of the foundational data systems, standards, and experimentation frameworks that serve as the source of truth for the entire company.

What you’ll do

• Architect the metrics that define user success on fal and build the measurement systems to track activation, retention, and time-to-value across both our self-serve and B2B pipelines.
• Develop a deep, causal understanding of how customers discover, adopt, and grow on the platform to identify key expansion levers and acquisition opportunities.
• Own the instrumentation standards and data contracts that serve as the source of truth for product telemetry and core behavioral schemas.
• Surface insights through rigorous deep-dives and experimentation to influence product direction, pricing models, and go-to-market initiatives.
• Develop scalable data products including automated experimentation frameworks and canonical schemas that enable teams to self-serve and move faster.

What we are looking for

• 5+ years of experience in data science or analytics roles-ideally with API products, developer tools, or B2B SaaS platforms
• Advanced-level SQL and proficiency in Python for analytics, modeling, and experimentation
• Strong proficiency with dbt and orchestration tools like Dagster or Airflow for production pipeline
• Proven ability to work across teams to influence product roadmap and strategy
• Experience establishing data quality and governance practices, including lineage, testing, and documentation.
• Demonstrated bias for action and the ability to thrive in fast-moving, ambiguous environments.
• A track record of shipping data products or features, not just dashboards. You think like a Product Manager and build like an Engineer

Nice-to-haves

• Experience working on developer-facing products or platforms.
• Exposure to infrastructure, performance, or cost-related data (e.g., cloud usage, compute efficiency).
• Experience operating in early-stage or fast-scaling environments.
• Interest or experience in architecting data systems designed for agentic consumption and AI-driven product workflow

Compensation

• $190,000 - $230,000 + equity + comprehensive benefits package

What we offer at fal

• Interesting and challenging work
• A lot of learning and growth opportunities
• We offer visa sponsorship and will help you relocate to San Francisco.
• Health, dental, and vision insurance (US)
• Regular team events and offsites

About Octave

Octave is a modern mental health practice that offers both in-person and virtual therapy. We focus on making high-quality care more accessible and affordable by partnering with insurance providers. Our goal is to support clients and clinicians with a system built on equity, effectiveness, and sustainability.

About the Role

Octave is seeking to partner with existing group practices in California (Los Angeles, Sacramento, Riverside, San Francisco, and San Diego areas) that want to provide excellent mental health care, both in-person and virtually. As an Octave partner, your team will join a supportive community of clinicians focused on client-centered, evidence-based therapy. 

At Octave, we take care of all backend operations so your clinicians can focus on providing care. Our team manages credentialing, billing, marketing, technology support, and client referrals. This allows your clinicians to focus primarily on seeing the clients we connect them with and completing session documentation, removing the administrative burden from your group practice.

Your providers will support adult clients managing stress, anxiety, depression, and related concerns. We emphasize evidence based practices such as CBT and DBT with a strong commitment to culturally responsive care. 

What You’ll Do

• Provide therapy to adults dealing with stress, anxiety, depression, and other concerns.
• Use short-term, evidence-based approaches like CBT or DBT.
• Offer culturally responsive care.
• See clients virtually and/or in person (you must have your own office space to hold in-person sessions).

Qualifications for Group Practices

• Must have at least 3 or more fully licensed clinicians who would be joining Octave.
• Required to have your own office space and are willing to provide in-person services (located within the markets we're currently hiring for).
• All clinicians must be fully licensed (no associate or intern level providers).
• Licensed in California (LMFT, LPCC, LCSW, PsyD)
• Must be licensed and in good standing with your governing licensing board.
• At least 1 year of post-license experience (2+ preferred).
• Training in therapies such as CBT, DBT, trauma-focused treatments, or similar.
• Certain payor relationships may exclude you from employment.
• Master’s Degree or higher from an accredited school of Social Work, Psychology, Counseling or Marriage, or Family and Child Counseling.

Octave's Company Values: 

The below values drive our day-to-day operations.

• We’re human beings first. We operate with empathy and kindness – with our clients, with our collaborators, and with ourselves.
• People deserve better than status quo. We’re willing to tackle the intractable problems, no matter how big, because someone should. We ask big questions, we craft big solutions, and we challenge ourselves and others to make it happen.
• No bystanders. No stars. No tourists. Each person has been selected to be here, and with that comes a responsibility to bring your expertise, share your ideas, and help make this company better.
• Partnership paves the path ahead. We don’t operate in a silo, internally or externally. To transform the system, we believe in working with others to create something bigger, better, and stronger.
• Quality is crucial at scale. Quality is core to our business, and we refuse to sacrifice it as we grow.
• Progress is a process. In the pursuit of progress, we iterate, reflect, learn, adjust – and always leave things better than we found them.
• There are people behind every data point. We recognize that numbers tell only one part of the story, and we also do the work to understand impacts at the individual level.

Physical Requirements: 

• Prolonged periods sitting at a desk and working on a computer. 
• Must be able to frequently communicate with others through virtual meeting applications such as Zoom and Google Meet. 
• Must be able to observe and communicate information on a laptop. 
• Move up to 10 pounds on occasion. 
• Must be eligible to work in the United States without sponsorship now or in the future.
• Octave’s policy requires that all clinical sessions be conducted while you are physically located in the United States.

System Requirements

Octave IT supports these minimum system requirements for the most optimized experience using Octave telehealth tools:

• Computers that are Mac OS (Ventura or later) and Windows OS (10/11)
• Computers kept up with the most recent security updates
• An unmanaged device (a device not provided to you by another company/organization)

Additionally, Octave IT does not support using iOS, Android, or ChromeOS as your primary device.

Compensation

Starting pay for qualified applicants will depend on a combination of job-related factors, which may include education, training, experience, location, business needs, or market demands. The expected salary range for this role is set forth below and this range may be modified in the future.

The hourly compensation range for this role is listed below dependent on licensure:

In-Person Rates:
$122-$135/hour for master's level license.
$122-$145/hour for doctoral level license.

Virtual Rates:
$117-$130/hour for master's level license.
$117-$140/hour for doctoral level license.

How We Use Technology in Hiring:

As part of our hiring process, we may use technology tools, including AI-supported systems, to assist with reviewing applications or documenting interviews. These tools are designed to support our team, not replace human judgment, and final hiring decisions are always made by our team.

This job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice.

About the role

Are you passionate about using data and AI to shape the future of Faire's Go-to-Market (GTM) strategy, sales, and operations? If so, we want to talk to you! We're looking for a technical owner in our team to work closely with Revenue Operations, GTM Systems Engineers, Data Scientists, Product Analysts and Software Engineers to support revenue-driving GTM initiatives by designing and building scalable data capabilities that inform and drive insights. You will lead the path in using AI to boost productivity and transform how analytical engineering operates—leveraging AI to streamline data pipeline development and operations, improve GTM data consumption workflows (e.g., Snowflake, BI tools, Salesforce), and automate routine analytical tasks. In this role, you'll see a direct link between your work, company growth, and user satisfaction. You'll work with A tier players, work with one of the richest data sets in the world, use cutting-edge technology, and see your efforts affect products and SMBs regularly. 

Join us and be part of a team that values data-driven innovation and excellence.

What You Will Do

The ideal candidate will possess a perfect blend of data engineering technical expertise and the product sense to be able to translate internal customer needs into practical solutions. They will have strong skills in developing and supporting large-scale, high-performance data storage and processing systems. They should have a proven track record of collaborating with key stakeholders to execute on GTM data initiatives. 

• Lead cross-team and/or multi-phase projects from design through implementation, fostering collaboration with cross-functional partners.
• Lead the technical execution in building scalable data models and pipelines for a GTM domain defining the architecture, data requirements, and roadmap (e.g., Contact Scoring, Lead Qualification, Sales Pipeline, Sales Enablement).
• Build data pipelines with rigorous approaches to data quality, validation, testing, and observability.
• Redefine how we build, maintain, and scale data systems by embedding AI into our daily workflows.
• Optimize processes for operational excellence in project management and system reliability.
• Ensure security standards are met within data systems and workflows. 
• Ensure and promote data quality standards for accurate and reliable insights.
• Embrace emerging technologies with enthusiasm.

Minimum qualifications

• 4+ years experience in analytics & data engineering roles focused on data modeling, large-scale data processing, and tool development for analytics or data science use cases.
• Demonstrated communication and leadership skills, with a history of initiating and steering successful projects across multiple stakeholders.
• Production-level development experience in Python.
• Strong SQL skills with demonstrable competencies in designing well-architected data models and optimizing query performance.
• Deep experience and knowledge of data warehousing concepts, ETLs, big data technologies, and analytics platforms.
• Experience with Airflow, Docker, DBT or similar analytics workflow tools.
• BA/BS degree in Computer Science, Math, Physics, or a related technical field. 

Nice-to-haves

• Experience with GTM specific data modeling using a BI tool such as Looker or similar while leveraging conversational analytics. Build and own the GTM semantic layer.
• Hands-on experience with GTM-specific data and platform apps, including Reverse ETL (e.g., Hightouch), ELT/Ingestion tools (e.g., Fivetran), iPaaS/Integration platforms (e.g., Workato, N8N, Zapier), and CRM/GTM systems and tool (e.g., Salesforce, Clay).

Salary Range

San Francisco: the pay range for this role is $196,000 to $269,500 per year.

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.

About the Role

We’re looking for an engineer to join us and contribute to data infrastructure. You'll join a small, high-impact team responsible for architecting and scaling the core infrastructure behind distributed training pipelines, multimodal data catalogs, and intelligent processing systems that operate over petabytes of data.

Infrastructure is critical to us: it's the bedrock that enables every breakthrough. You'll work directly with researchers to accelerate experiments, develop new datasets, improve infrastructure efficiency, and enable key insights across our data assets.

If you're excited by distributed systems, large-scale data mining, open-source tools like Spark, Kafka, Beam, Ray, and Delta Lake, and enjoy building from the ground up, we'd love to hear from you.

Note: This is an "evergreen role" that we keep open on an on-going basis to express interest. We receive many applications, and there may not always be an immediate role that aligns perfectly with your experience and skills. Still, we encourage you to apply. We continuously review applications and reach out to applicants as new opportunities open. You are welcome to reapply if you get more experience, but please avoid applying more than once every 6 months. You may also find that we put up postings for singular roles for separate, project or team specific needs. In those cases, you're welcome to apply directly in addition to an evergreen role.

What You’ll Do

• Design, build, and operate scalable, fault-tolerant infrastructure for LLM Research: distributed compute, data orchestration, and storage across modalities.
• Develop high-throughput systems for data ingestion, processing, and transformation — including training data catalogs, deduplication, quality checks, and search.
• Build systems for traceability, reproducibility, and robust quality control at every stage of the data lifecycle.
• Implement and maintain monitoring and alerting to support platform reliability and performance.
• Collaborate with research teams to unlock new features, improve data quality, and accelerate training cycles.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, engineering, or similar.
• Proficiency in at least one backend language (we use Python or Rust).
• Are fluent in distributed compute frameworks such as Apache Spark or Ray.
• Are deeply familiar with cloud infrastructure, data lake architectures, and batch and streaming pipelines.
• Comfort operating across the stack and owning projects end-to-end.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.

Preferred qualifications — we encourage you to apply if you meet some but not all of these:

• Have hands-on experience with Kafka, dbt, Terraform, and Airflow.
• Have experience building a web crawler.
• Have extensive experience understanding and scaling deduplication, data mining, and search.
• Have strong knowledge of file formats and storage systems (e.g., Parquet, Delta Lake, etc.) and how they impact performance and scalability.
• Are proactive about documentation, testing, and empowering your teammates with good tooling.

Logistics

• Location: This role is based in San Francisco, California. 
• Compensation: Depending on background, skills and experience, the expected annual salary range for this position is $350,000 - $475,000 USD.
• Visa sponsorship: We sponsor visas. While we can't guarantee success for every candidate or role, if you're the right fit, we're committed to working through the visa process together.
• Benefits: Thinking Machines offers generous health, dental, and vision benefits, unlimited PTO, paid parental leave, and relocation support as needed.

Reporting to the Data Engineering Manager, the Senior Analytics Engineer (Finance) will be the dedicated data partner for Checkr's Finance organization, owning the models, pipelines, and governance that power how we measure and forecast the business. You will build and maintain the trusted data layer for ledgers, revenue reconciliation, COGS and margin analysis, and forecast model inputs — turning raw ERP and operational data into decision-ready assets. You'll work hand-in-hand with FP&A, Accounting, and Finance leadership, while helping shape one of Checkr's most exciting data platforms alongside a team that's actively building with the latest AI tooling.

What you'll do

• Design, build, and maintain scalable data models and pipelines that support revenue reconciliation, ledger integrity, COGS and margin analysis, and forecast model inputs
• Partner directly with FP&A, Accounting, and broader Finance stakeholders to translate business questions into well-governed, reusable data products in Looker and Omni
• Own the data contracts and quality checks for finance-critical datasets, establishing governance standards that make numbers audit-ready and trusted across the company
• Model data out of ERP systems (NetSuite, Oracle, and similar) and integrate it with operational sources to produce a unified financial view of the business
• Drive adoption of AI-powered workflows and tooling within the analytics engineering stack to accelerate pipeline development, documentation, and stakeholder self-service
• Partner with Data Engineering peers to evolve Checkr's modern data platform, championing best practices in testing, documentation, and lineage

What you bring

• 5+ years of experience in an Analytics Engineering, Data Engineering, or Data Analyst role, ideally with meaningful exposure to Finance or Accounting stakeholders
• Expert SQL and strong experience building production-grade data models and pipelines
• Hands-on experience modeling data from ERP systems such as NetSuite, Oracle, or similar, and strong familiarity with financial concepts like GL, revenue recognition, COGS, and margin
• Experience with BI and modeling tools such as Looker and Omni, and the ability to design semantic layers that scale with stakeholder needs
• Excellent communication skills and a track record of translating ambiguous Finance requirements into technical solutions
• Experience with dbt and Snowflake, and enthusiasm for working with AI-powered development tools, is a strong plus
• An A-player mindset with a strong bias for action: you raise the bar, move with urgency, stay resilient through ambiguity, and take ownership to deliver meaningful outcomes.

What you’ll get

• A fast-paced and collaborative environment
• Learning and development allowance
• Competitive cash and equity compensation, and opportunity for advancement
• 100% medical, dental, and vision coverage
• Up to $25K reimbursement for fertility, adoption, and parental planning services
• Flexible PTO policy
• Monthly wellness stipend

Senior Associate, Software Engineer – Data Platform & AI Applications
San Francisco, California 

The Opportunity 

Jordan Park is seeking a Senior Associate to help build and extend our enterprise data platform and AI application ecosystem.

This role combines advanced data engineering, cloud infrastructure, DevOps discipline, and applied AI development. The Senior Associate will operate as a senior individual contributor — owning major platform components while partnering closely with leadership and business teams. This is a hands-on technical role with meaningful scope and influence.

In this role, you will be responsible for enabling trusted analytics across the firm, integrating proprietary data into AI systems, automating operational workflows and creating custom apps and tooling.

Your Role

• Data Platform Engineering
  • Design, build, and maintain production-grade SQL data pipelines (e.g., dbt + Snowflake + Python)
  • Develop and manage orchestration workflows (Airflow or equivalent)
  • Create data ingestion processes and API integrations

• AI Application Development
  • Build and develop the use of AI in the form of: LLM apps, RAG architectures, Custom GPTs & Agent systems
  • Integrate proprietary firm data securely into AI workflows
  • Establish observability standards
  • Translate AI experimentation into production-grade tools

• Cloud Infrastructure (AWS)
  • Support AWS-based infrastructure including VPCs, ECS, EKS, S3, etc.
  • Containerize services using Docker and manage workloads in Kubernetes
  • Establish logging, monitoring standards

• Python Development & Automation
  • Develop Python-based tools for API integrations, data transformations, ad-hoc in-house applications & tooling
  • Build reusable modules and maintain code standards
  • Create automation solutions that reduce manual operational effort

• Projects & Communication
  • Partner cross-functionally with Investment, Client Advisory, Operations, and Finance teams
  • Be self-directed in project management and delivery
  • Translate business requirements into technical architecture
  • Deliver executive-ready summaries of platform and AI initiatives

Qualifications

Required:

• Bachelors degree with 5–8 years of experience in: data engineering, platform engineering, cloud infrastructure
• Requisite experience in AI Application development including LLM APIs and RAG Pipelines
• AWS experience including Docker and Kubernetes
• Advanced Python proficiency
• Advanced SQL and data modeling
• Experience implementing CI/CD and DevOps best practices
• Strong written and executive communication skills
• Experience in wealth management, asset management, or fintech
• A positive, helpful and can-do attitude

Preferred:

• Salesforce data integration experience
• Experience designing secure AI architectures
• Experience mentoring junior engineers

Compensation & Benefits

Compensation at Jordan Park includes a base salary, discretionary year-end bonus, benefits and other perks.  We apply a total reward philosophy when determining compensation terms. The expected base salary for this role in San Francisco, California will range from $125,000 to $160,000 per year commensurate with experience, job-related skills, relevant education, licenses and certifications, and other business and organizational needs. 

Our benefits package includes:  

• Medical, dental & vision insurance – 100% of premium covered for employees
• 401k participation with employer contribution
• Generous paid time off 
• Commuter benefits program (pre-paid tax dollars towards your commute)
• Fitness Reimbursement
• Annual Professional Development Stipend 

Associate, Software Engineer – Data Platform
San Francisco, California

The Opportunity 

Jordan Park is seeking a Technology & Data Engineering Associate to advance our data platform and data quality initiatives. This role operates at the intersection of data engineering, analytics, data quality management, and AI enablement.

The Associate will design, build, and maintain production-grade data systems while partnering directly with executives, investment teams, operations, and client service professionals to drive measurable business outcomes. This is a hands-on technical role with meaningful ownership and visibility.

Your Role

• Data Platform
  • Design, build, and maintain production-grade SQL data pipelines (e.g., dbt + Snowflake + Python)
  • Develop and manage orchestration workflows (Airflow or equivalent)
  • Create data ingestion processes and API integrations
  • Monitor pipeline reliability and performance; proactively resolve issues

• Data Quality & Controls
  • Develop deep expertise in firm data, systems, and processes
  • Design reconciliation frameworks across systems and automate data validation/exception reporting
  • Perform detailed variance analysis and root cause investigations
  • Partner with operations and finance teams to ensure reporting integrity

• Analytics & Business Intelligence
  • Architect and maintain Tableau dashboards used across the firm
  • Train and support internal stakeholders on self-service analytics

• Python Development & Automation
  • Develop Python-based tools for API integrations, data transformations, ad-hoc in-house applications & tooling
  • Build reusable modules and maintain code standards
  • Create automation solutions that reduce manual operational effort

• Projects & Communication
  • Work closely with the Chief Technology Officer (CTO) to deliver clear executive summaries of data findings, operational trends, etc.
  • Document project discovery and project management artifacts
  • Translate technical work into business impact
  • Present findings to senior leadership

Qualifications

Required:

• Bachelors degree with 3–5 years of experience in Data Engineering, Analytics, Business Intelligence or related technical role
• Advanced SQL proficiency (production-level experience)
• Experience with dbt and Snowflake (or similar modern data stack)
• Strong Python skills for data engineering and automation
• Experience with Tableau or similar BI platform
• Strong written and verbal communication skills
• A positive, helpful and can-do attitude

Preferred:

• Experience in wealth management, asset management, fintech, or financial services
• Familiarity with Salesforce reporting and CRM data models
• Experience building AI workflows or internal GPT tools
• Experience with cloud infrastructure (AWS preferred)

Compensation & Benefits

Compensation at Jordan Park includes a base salary, discretionary year-end bonus, benefits and other perks.  We apply a total reward philosophy when determining compensation terms. The expected base salary for this role in San Francisco, California will range from $115,000 to $150,000 per year commensurate with experience, job-related skills, relevant education, licenses and certifications, and other business and organizational needs. 

Our benefits package includes:  

• Medical, dental & vision insurance – 100% of premium covered for employees
• 401k participation with employer contribution
• Generous paid time off 
• Commuter benefits program (pre-paid tax dollars towards your commute)
• Fitness Reimbursement
• Annual Professional Development Stipend 

What The Role Is

We are seeking a talented Senior Data Engineer to design, build, and maintain our data infrastructure supporting mission-critical energy operations. You'll work at the intersection of renewable energy and data, developing pipelines that process everything from real-time asset performance data to complex trading and risk analytics. This hybrid role offers the opportunity to make a direct impact on clean energy operations while working with a cutting-edge data stack including Snowflake, Dagster, dbt, Modal, and GitLab.

What You'll Be Doing

Analytics Infrastructure & Data Warehouse Management

• Design, deploy, and maintain scalable data infrastructure to support enterprise analytics and reporting needs
• Manage Snowflake instances, including performance tuning, security configuration, and capacity planning for growing data volumes
• Optimize query performance and resource utilization to control costs and improve processing speed

Data Pipeline Development & Orchestration

• Build and orchestrate complex ETL/ELT workflows using Dagster to ensure reliable, automated data processing for asset management and energy trading
• Develop robust data pipelines that handle high-volume, time-sensitive energy market data and asset generation and performance metrics
• Implement workflow automation and dependency management for critical business operations

Data Transformation & Analytics Support

• Develop and maintain dbt models to transform raw data into business-ready analytical datasets and dimensional models
• Create efficient SQL-based transformations for complex energy market calculations and asset performance metrics
• Support advanced analytics initiatives through proper data preparation and feature engineering

Data Quality & Governance

• Implement comprehensive data validation, testing, and monitoring frameworks to ensure accuracy and consistency across all energy and financial data assets
• Establish data lineage tracking and privacy controls to meet regulatory compliance requirements in the energy sector
• Develop alerting and monitoring systems for data pipelines, including error handling, SLA monitoring, and incident response

CI/CD & DevOps

• Lead continuous integration and deployment initiatives for Dagster and dbt pipelines, and Streamlit/Gradio application deployments to Linux servers
• Implement automated testing and deployment automation for data pipelines and analytics applications
• Manage version control and infrastructure as code practices

Cross-functional Collaboration

• Partner with Analytics Engineers, Data Scientists, and business stakeholders to understand requirements and deliver solutions
• Work closely with asset management and trading groups to ensure real-time data availability for market operations and risk calculations
• Collaborate with credit risk teams to develop data models supporting financial analysis and regulatory reporting
• Translate business requirements into technical solutions and communicate data insights to stakeholders

Documentation & Security

• Create and maintain technical documentation, data dictionaries, and onboarding materials for data assets
• Implement role-based access controls, data encryption, and security best practices across the data stack
• Monitor and optimize cloud infrastructure costs, implement resource allocation strategies, and provide cost forecasting

What You'll Bring

• Experience: 4+ years of hands-on data engineering experience in production environments
• Education: Bachelor's degree in Computer Science, Engineering, or a related field
• Data Orchestration: Proficiency in Dagster for pipeline scheduling, dependency management, and workflow automation; Airflow experience a plus
• Cloud Data Warehousing: Advanced-level Snowflake administration, including virtual warehouses, clustering, security, and cost optimization
• Data Transformation: Proficiency in dbt for data modeling, testing, documentation, and version control of analytical transformations
• Programming: Strong Python and SQL skills for data processing and automation
• CI/CD: 3+ years of experience with continuous integration and continuous deployment practices and tools; proficiency in GitLab CI/CD required (GitHub Actions experience a plus)
• Database Technologies: Advanced SQL skills, database design principles, and experience with multiple database platforms
• Cloud Platforms: Proficiency in AWS/Azure/GCP data services, storage solutions (S3, Azure Blob, GCS), and infrastructure as code
• Data Integration: Experience with APIs, various data connectors, and formats
• Data Security: Understanding of data security best practices, access controls, encryption, and role-based access management
• AI & LLM Proficiency: Practical experience integrating and leveraging large language models (e.g., OpenAI, Anthropic, or open-source models) within data workflows; ability to apply LLMs efficiently and securely, with awareness of data privacy boundaries, prompt injection risks, and responsible AI usage in production environments
• Problem-Solving: Strong analytical and troubleshooting skills with attention to detail
• Communication: Ability to work effectively with both technical and business stakeholders
• Work Style: Comfortable with a hybrid work environment (2-3 days in office); highly autonomous and self-directed with a strong work ethic and growth mindset; exceptional attention to detail; able to take ownership of projects end-to-end with minimal oversight

What Would Be Nice

• Experience in the energy, utilities, or financial services industries
• Knowledge of energy trading concepts, market data, or asset management
• Familiarity with data governance frameworks and regulatory compliance (SOX, GDPR, CCPA)
• Experience with infrastructure as code (Terraform, CloudFormation)

Clearway will not sponsor non-immigrant visas for this position (H-1B, TN, E-3, etc.).

#LI-Hybrid

What The Role Is

We are seeking a talented Senior Data Engineer to design, build, and maintain our data infrastructure supporting mission-critical energy operations. You'll work at the intersection of renewable energy and data, developing pipelines that process everything from real-time asset performance data to complex trading and risk analytics. This hybrid role offers the opportunity to make a direct impact on clean energy operations while working with a cutting-edge data stack including Snowflake, Dagster, dbt, Modal, and GitLab.

What You'll Be Doing

Analytics Infrastructure & Data Warehouse Management

• Design, deploy, and maintain scalable data infrastructure to support enterprise analytics and reporting needs
• Manage Snowflake instances, including performance tuning, security configuration, and capacity planning for growing data volumes
• Optimize query performance and resource utilization to control costs and improve processing speed

Data Pipeline Development & Orchestration

• Build and orchestrate complex ETL/ELT workflows using Dagster to ensure reliable, automated data processing for asset management and energy trading
• Develop robust data pipelines that handle high-volume, time-sensitive energy market data and asset generation and performance metrics
• Implement workflow automation and dependency management for critical business operations

Data Transformation & Analytics Support

• Develop and maintain dbt models to transform raw data into business-ready analytical datasets and dimensional models
• Create efficient SQL-based transformations for complex energy market calculations and asset performance metrics
• Support advanced analytics initiatives through proper data preparation and feature engineering

Data Quality & Governance

• Implement comprehensive data validation, testing, and monitoring frameworks to ensure accuracy and consistency across all energy and financial data assets
• Establish data lineage tracking and privacy controls to meet regulatory compliance requirements in the energy sector
• Develop alerting and monitoring systems for data pipelines, including error handling, SLA monitoring, and incident response

CI/CD & DevOps

• Lead continuous integration and deployment initiatives for Dagster and dbt pipelines, and Streamlit/Gradio application deployments to Linux servers
• Implement automated testing and deployment automation for data pipelines and analytics applications
• Manage version control and infrastructure as code practices

Cross-functional Collaboration

• Partner with Analytics Engineers, Data Scientists, and business stakeholders to understand requirements and deliver solutions
• Work closely with asset management and trading groups to ensure real-time data availability for market operations and risk calculations
• Collaborate with credit risk teams to develop data models supporting financial analysis and regulatory reporting
• Translate business requirements into technical solutions and communicate data insights to stakeholders

Documentation & Security

• Create and maintain technical documentation, data dictionaries, and onboarding materials for data assets
• Implement role-based access controls, data encryption, and security best practices across the data stack
• Monitor and optimize cloud infrastructure costs, implement resource allocation strategies, and provide cost forecasting

What You'll Bring

• Experience: 4+ years of hands-on data engineering experience in production environments
• Education: Bachelor's degree in Computer Science, Engineering, or a related field
• Data Orchestration: Proficiency in Dagster for pipeline scheduling, dependency management, and workflow automation; Airflow experience a plus
• Cloud Data Warehousing: Advanced-level Snowflake administration, including virtual warehouses, clustering, security, and cost optimization
• Data Transformation: Proficiency in dbt for data modeling, testing, documentation, and version control of analytical transformations
• Programming: Strong Python and SQL skills for data processing and automation
• CI/CD: 3+ years of experience with continuous integration and continuous deployment practices and tools; proficiency in GitLab CI/CD required (GitHub Actions experience a plus)
• Database Technologies: Advanced SQL skills, database design principles, and experience with multiple database platforms
• Cloud Platforms: Proficiency in AWS/Azure/GCP data services, storage solutions (S3, Azure Blob, GCS), and infrastructure as code
• Data Integration: Experience with APIs, various data connectors, and formats
• Data Security: Understanding of data security best practices, access controls, encryption, and role-based access management
• AI & LLM Proficiency: Practical experience integrating and leveraging large language models (e.g., OpenAI, Anthropic, or open-source models) within data workflows; ability to apply LLMs efficiently and securely, with awareness of data privacy boundaries, prompt injection risks, and responsible AI usage in production environments
• Problem-Solving: Strong analytical and troubleshooting skills with attention to detail
• Communication: Ability to work effectively with both technical and business stakeholders
• Work Style: Comfortable with a hybrid work environment (2-3 days in office); highly autonomous and self-directed with a strong work ethic and growth mindset; exceptional attention to detail; able to take ownership of projects end-to-end with minimal oversight

What Would Be Nice

• Experience in the energy, utilities, or financial services industries
• Knowledge of energy trading concepts, market data, or asset management
• Familiarity with data governance frameworks and regulatory compliance (SOX, GDPR, CCPA)
• Experience with infrastructure as code (Terraform, CloudFormation)

Clearway will not sponsor non-immigrant visas for this position (H-1B, TN, E-3, etc.).

#LI-Hybrid

What The Role Is

We are seeking a talented Senior Data Engineer to design, build, and maintain our data infrastructure supporting mission-critical energy operations. You'll work at the intersection of renewable energy and data, developing pipelines that process everything from real-time asset performance data to complex trading and risk analytics. This hybrid role offers the opportunity to make a direct impact on clean energy operations while working with a cutting-edge data stack including Snowflake, Dagster, dbt, Modal, and GitLab.

What You'll Be Doing

Analytics Infrastructure & Data Warehouse Management

• Design, deploy, and maintain scalable data infrastructure to support enterprise analytics and reporting needs
• Manage Snowflake instances, including performance tuning, security configuration, and capacity planning for growing data volumes
• Optimize query performance and resource utilization to control costs and improve processing speed

Data Pipeline Development & Orchestration

• Build and orchestrate complex ETL/ELT workflows using Dagster to ensure reliable, automated data processing for asset management and energy trading
• Develop robust data pipelines that handle high-volume, time-sensitive energy market data and asset generation and performance metrics
• Implement workflow automation and dependency management for critical business operations

Data Transformation & Analytics Support

• Develop and maintain dbt models to transform raw data into business-ready analytical datasets and dimensional models
• Create efficient SQL-based transformations for complex energy market calculations and asset performance metrics
• Support advanced analytics initiatives through proper data preparation and feature engineering

Data Quality & Governance

• Implement comprehensive data validation, testing, and monitoring frameworks to ensure accuracy and consistency across all energy and financial data assets
• Establish data lineage tracking and privacy controls to meet regulatory compliance requirements in the energy sector
• Develop alerting and monitoring systems for data pipelines, including error handling, SLA monitoring, and incident response

CI/CD & DevOps

• Lead continuous integration and deployment initiatives for Dagster and dbt pipelines, and Streamlit/Gradio application deployments to Linux servers
• Implement automated testing and deployment automation for data pipelines and analytics applications
• Manage version control and infrastructure as code practices

Cross-functional Collaboration

• Partner with Analytics Engineers, Data Scientists, and business stakeholders to understand requirements and deliver solutions
• Work closely with asset management and trading groups to ensure real-time data availability for market operations and risk calculations
• Collaborate with credit risk teams to develop data models supporting financial analysis and regulatory reporting
• Translate business requirements into technical solutions and communicate data insights to stakeholders

Documentation & Security

• Create and maintain technical documentation, data dictionaries, and onboarding materials for data assets
• Implement role-based access controls, data encryption, and security best practices across the data stack
• Monitor and optimize cloud infrastructure costs, implement resource allocation strategies, and provide cost forecasting

What You'll Bring

• Experience: 4+ years of hands-on data engineering experience in production environments
• Education: Bachelor's degree in Computer Science, Engineering, or a related field
• Data Orchestration: Proficiency in Dagster for pipeline scheduling, dependency management, and workflow automation; Airflow experience a plus
• Cloud Data Warehousing: Advanced-level Snowflake administration, including virtual warehouses, clustering, security, and cost optimization
• Data Transformation: Proficiency in dbt for data modeling, testing, documentation, and version control of analytical transformations
• Programming: Strong Python and SQL skills for data processing and automation
• CI/CD: 3+ years of experience with continuous integration and continuous deployment practices and tools; proficiency in GitLab CI/CD required (GitHub Actions experience a plus)
• Database Technologies: Advanced SQL skills, database design principles, and experience with multiple database platforms
• Cloud Platforms: Proficiency in AWS/Azure/GCP data services, storage solutions (S3, Azure Blob, GCS), and infrastructure as code
• Data Integration: Experience with APIs, various data connectors, and formats
• Data Security: Understanding of data security best practices, access controls, encryption, and role-based access management
• AI & LLM Proficiency: Practical experience integrating and leveraging large language models (e.g., OpenAI, Anthropic, or open-source models) within data workflows; ability to apply LLMs efficiently and securely, with awareness of data privacy boundaries, prompt injection risks, and responsible AI usage in production environments
• Problem-Solving: Strong analytical and troubleshooting skills with attention to detail
• Communication: Ability to work effectively with both technical and business stakeholders
• Work Style: Comfortable with a hybrid work environment (2-3 days in office); highly autonomous and self-directed with a strong work ethic and growth mindset; exceptional attention to detail; able to take ownership of projects end-to-end with minimal oversight

What Would Be Nice

• Experience in the energy, utilities, or financial services industries
• Knowledge of energy trading concepts, market data, or asset management
• Familiarity with data governance frameworks and regulatory compliance (SOX, GDPR, CCPA)
• Experience with infrastructure as code (Terraform, CloudFormation)

Clearway will not sponsor non-immigrant visas for this position (H-1B, TN, E-3, etc.).

#LI-Hybrid

At Ripple, we're building a world where value moves like information does today. It's big and bold, and we're already doing it. Through our crypto solutions for financial institutions, businesses, governments, and developers, we improve the global financial system. We craft greater economic fairness and opportunity for more people in more places globally. We perform the best work of our careers and develop our skills with colleagues who have our backs.

If you're ready to see your impact and embrace meaningful new challenges, join us, and build real world value.

The Work:

We're seeking a Senior Staff Engineer to act as the technical architect and strategic leader for Ripple's data platform. This role is one of the highest-impact individual contributor positions on the Data team. You will build a coherent, unified view of Ripple's data across all business units. You will establish the definitive source of truth for company metrics and lead our "AI for Data" transformation. You will collaborate across data teams and work directly with senior leadership in Data, Product, Engineering, and Go-to-Market to develop how Ripple understands itself through data. You won't just build systems; you will define the technical vision that connects them. This sits at the intersection of architecture, integration, governance, and applied AI.

What you'll do:

• Build "One View" of Ripple by serving as the main architect for a Unified Data Fabric. Construct a well-rounded view of Ripple's data platforms, pipelines, and tools. Craft the systems and interoperability standards that bring data together across all business units for leadership, product, and GTM teams.
• Compose and build the Ripple Metrics Store, a definitive, governed layer that serves as the single source of truth for business metrics across all business units.
• Define metrics governance, ownership models, and validation frameworks. Build and maintain the Metrics Glossary in partnership with the Data Science team, and collaborators crafting a shared language for how Ripple measures its business.
• Define and lead the AI roadmap for the Data organization setting the vision for how AI transforms the way Ripple builds, governs, and consumes data.
• Enable conversational analytics and intelligence by architecting AI Agents that allow non-technical collaborators to self-serve insights directly from the data platform.
• Automate data engineering workflows by encouraging use of AI-powered coding assistants for SQL/ETL generation. Define the standards, validation frameworks, and guidelines for AI-generated code across the team.
• Lead cross-team build reviews, setting the technical bar for data architecture decisions across the organization.
• Mentor and up-level engineers across data teams, raising the standard for system building, code quality, and engineering rigor.
• Act as the main technical consultant to senior leadership across Data, Product, and Engineering regarding long-term technical bets, platform investments, and build-vs-buy decisions.

What you'll bring:

• 14+ years of experience in data engineering, data architecture, or related software engineering fields, with a significant portion spent in senior technical leadership or architect roles.
• Having a track record of bringing together data ecosystems from multiple business units, you have built the connections that convert isolated data platforms into a consistent entity.
• Deep expertise in modern data architecture, data mesh, lakehouse patterns, with hands-on experience in technologies like Spark, Kafka, Airflow, dBT, Databricks, and cloud-native data services (AWS, GCP, or Azure).
• Experienced in metrics governance and semantic layers, you have established or planned metrics stores, business glossaries, or comparable systems that provide an organization's source of truth.
• Demonstrated experience with AI/ML in data engineering contexts, whether building feature stores, deploying AI-powered data quality monitoring, integrating LLMs into developer workflows, or crafting agentic systems for data operations.
• Architectural vision paired with execution ability, you can whiteboard a multi-year platform strategy and also get into the code to prototype critical path components.
• With outstanding communication and influence skills, you comfortably present to VP/C-level audiences. You translate technical trade-offs into business language and build consensus among teams with competing priorities.
• A collaborative, ego-free approach to leadership, you lead through influence and technical credibility, not through title.
• Experience with blockchain data, on-chain analytics, or large-scale financial/payments data is a plus.
• Direct involvement with LLM-enabled developer tools and agents, AI code synthesis, RAG-style data catalogs, natural language querying interfaces, or agent orchestration solutions is beneficial.
• BS or equivalent experience in Computer Science or similar.

The Role:

We are seeking an experienced AI Solutions & Delivery Engineer to join our Business Controls Group (BCG) as a hands-on technical leader focused on applying Artificial Intelligence, including Generative AI and Agentic AI, to accelerate and elevate first line of defense (1LOD) risk and control operations.

The ideal candidate will possess a blend of business and technical savvy, a vision for what AI can unlock within a controls environment, and the drive to transform that vision into reality. You will be responsible for partnering with stakeholders across BCG: listening, facilitating, hands-on developing, and delivering prioritized AI solutions that augment and elevate the capabilities of our risk and controls professionals.

You will focus on building AI-powered solutions for specific BCG initiatives, including control testing, horizontal risk-related reporting, and other 1LOD process acceleration activities, and manage a portfolio of AI initiatives from ideation through production deployment. This role exists to increase the quality, velocity, and scalability of how our Business Controls teams execute their work.

You will also serve as technical lead for the Business Controls AI Engineering function, acting as a system-level thinker, designing, orchestrating, building, and deploying AI agents, tools, and enterprise data integrations for BCG and 1LOD risk management teams within the guardrails of SoFi’s governance framework.

What you’ll do:

AI Strategy & Solutions Delivery

• Partner with BCG leadership to define the AI solutions vision and roadmap for 1LOD risk and controls, based on business use case prioritization and scorecard evaluation.

• Identify and implement opportunities where AI can uplift risk and control processes and practices, with an initial focus on control testing velocity and business controls reporting.

• Design, develop, and deploy AI-powered solutions (including LLM-based agents, automated evidence analysis, and intelligent workflow tools) that deliver measurable improvements to BCG operations.

• Translate ambiguous, fast-evolving AI work into clear narratives, artifacts, and decision frameworks for senior and executive leaders.

• Manage the end-to-end AI product lifecycle, from ideation and requirements gathering through development, testing, launch, and post-launch optimization.

Stakeholder Engagement & Enablement

• Partner with Business Controls teams, risk professionals, technology teams, and other BCG stakeholders to drive development, adoption, and continuous improvement of AI solutions.

• Enable risk professionals’ adoption of AI tools by providing training, documentation, and hands-on support to increase comfort and proficiency across the organization.

• Partner with enterprise AI and platform engineering teams to ensure AI solutions are built in compliance with best practices, enterprise policies, and internal standards.

• Contribute to the uplifting and upscaling of AI capabilities across BCG, assisting in the responsible adoption of best-in-class AI platforms within the organization.

Performance & Metrics

• Define key performance indicators (KPIs) and metrics for AI solutions, monitor performance post-launch, and iterate based on data-driven insights.

• Ensure AI solutions deliver measurable business value, including reductions in cycle time, improvements in testing coverage, and enhanced reporting accuracy.

• Establish and maintain dashboards that communicate AI initiative progress, adoption, and impact to BCG leadership.

Governance, Risk & Compliance

• Manage end-to-end Generative AI and Responsible AI governance for BCG solutions, including documentation, validation, monitoring, explainability, and adherence to internal policies and emerging AI regulations.

• Demonstrate critical thinking and the ability to clearly articulate risks and dimension impact when evaluating AI use cases and deployments.

• Ensure all AI solutions align with enterprise governance standards, regulatory expectations, and model risk management requirements.

Technical Leadership

• Serve as technical lead for the Business Controls AI Engineering function.

• Evaluate, select, and integrate AI/ML tools and platforms that best serve BCG’s operational needs.

• Break ambiguous problems into clear, measurable components and define actionable AI development plans.

• Demonstrate sound judgment when balancing innovation against risk in a regulated financial services environment.

What you’ll need:

• 8+ years of experience in risk management, controls, compliance, or technology roles, with at least 3+ years focused on AI/ML products, applied AI solutions, or AI-driven process transformation.

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent work experience).

• Demonstrated experience in ambiguous or emerging domains, including leadership in defining, launching, and scaling AI solutions that deliver significant business impact.

• Proven experience working at a senior individual contributor level or equivalent in a regulated organization, preferably in financial services.

• Strong understanding of the financial services industry, including 1LOD risk management, control testing, and business controls operations.

• Hands-on experience with AI/ML concepts including LLMs, Generative AI, Agentic AI, supervised/unsupervised learning, model monitoring, and responsible AI practices.

• Understanding of data governance, data pipelines, and MLOps principles.

• Strong communication and executive stakeholder engagement skills, with the ability to explain complex ideas clearly to non-experts at different audience levels.

• Experience with regulatory expectations, model governance, and risk management in a banking or financial services context.

• Strategic thinker with a strong analytical and problem-solving mindset.

• Rapid self-learner in unfamiliar domains with a bias toward action and delivery.

Nice to have:

• Experience with Snowflake, dbt, or other modern data platform tools used in enterprise data environments.

• Familiarity with LLM tooling, RAG systems, AI agent frameworks, or AI-assisted data workflows.

• Financial services experience across domains such as Credit, Fraud, Collections, or Operational Risk.

• Experience with GRC platforms and control testing methodologies.

• Experience implementing AI governance frameworks at scale.

• AWS experience (S3, Glue, Lambda) and infrastructure-as-code familiarity.

The Role

The Borrow Data Science team is seeking a Senior Data Scientist who will help drive growth in our Lending businesses by transforming raw data into a compelling narrative for success. This is an exciting role for a proactive data scientist who is naturally curious and possesses a deep-seated drive to uncover insights before they are even requested.

Success in this role hinges on your ability to act as a strategic partner; you are not just a technical executor but an analytical storyteller who can bridge the gap between complex data and business strategy. While you have the technical aptitude to leverage tools like SQL, Python, dbt, and Airflow to build robust pipelines in Snowflake, your true impact lies in your ability to lead cross-functional initiatives and influence the product roadmap through data-driven advocacy.

What You’ll Do

• Drive Proactive Insights: Use your curiosity to dig deep into data, identifying trends and opportunities that others might miss, and proactively presenting these findings to leadership.
• Champion AI Productivity: Act as a pioneer for AI integration within the team. Identify and implement AI tools and workflows that streamline data engineering tasks, accelerate analysis, and drive outsized business impact.
• Analytical Storytelling: Translate complex quantitative analyses into simple, persuasive narratives that enable business leaders to make informed, high-stakes decisions.
• Strategic Leadership: Partner with cross-functional teams to lead the optimization of core product decisions, including funnel conversion, experimentation, and unit economics.
• Build Technical Foundations: Design and maintain scalable reporting pipelines in Snowflake using dbt and Airflow, ensuring business logic is efficiently encapsulated for company-wide use.
• Champion KPI Consistency: Create frameworks that ensure a "single source of truth" for KPIs across various platforms and BI tools.
• Mentor and Innovate: Stay ahead of industry trends and technical features to continuously improve our data stack, while mentoring peers on best practices in analytical rigor.

What You’ll Need

• Education: B.S., M.S., or M.B.A. in a quantitative field (Statistics, Economics, Engineering, CS, Mathematics, etc.).
• Experience: 5+ years of experience in an Analytics, Data Science, or Engineering role, preferably within Finance or Tech.
• Communication & Influence: Exceptional communication skills with a proven track record of influencing stakeholders and leading projects from conception to completion.
• Innate Curiosity: A self-starter mindset with a passion for problem-solving and an ability to thrive in dynamic, ambiguous environments.
• Technical Proficiency: Strong programming skills in SQL and Python, with hands-on experience using dbt and Airflow to build data pipelines in Snowflake.
• Data Visualization: Experience building intuitive and impactful dashboards in tools like Tableau to support your storytelling.
• Collaboration: Strong relationship-building skills and the ability to work seamlessly across diverse, cross-functional teams