ABOUT THE TEAM

The People Analytics team is at the forefront of transforming how we understand and optimize our workforce. We are a dynamic group of analytical thinkers and storytellers who leverage advanced analytics and robust methodologies to create strategic insights from complex HR data. Collaborating closely with HR Business Partners, Program Management Teams, Business Operations, Talent Acquisition and various cross functional stakeholders we provide actionable insights that drive informed decisions across the entire employee lifecycle; from optimizing talent acquisition and development to enhancing engagement and retention. Our work directly shapes a thriving employee experience, fuels organizational growth, and ensures our people strategy is truly data-driven.

ABOUT THE JOB

This role is central to our People Data & Analytics team, where you will be instrumental in building and maintaining the robust data infrastructure that powers our strategic insights. You'll own the full data lifecycle, from ensuring accurate ingestion and integration of diverse HR data sources, to designing, developing, and optimizing data models and pipelines. Your primary objective will be to transform raw, disparate information into clean, reliable, and analytics-ready datasets, empowering our People Analysts and business stakeholders to unlock deeper understanding of our workforce, enhance employee experience, and drive data-driven decision-making. Success in this role requires a strong technical foundation, meticulous attention to data quality, and a passion for crafting efficient data solutions.

WHAT YOU'LL DO

• Design, build, and optimize robust ETL/ELT pipelines to reliably ingest, integrate, and transform diverse people data from various HR systems (HRIS, ATS, LMS, etc.) into our data platform.
• Develop, maintain, and govern scalable and secure data models, schemas, and ontologies specifically for people analytics, ensuring data quality, consistency, and accessibility for downstream consumption.
• Contribute to the strategic design, development, and evolution of our people data platform and tooling, advocating for engineering best practices, automation, and a scalable analytics ecosystem (e.g., leveraging SQLMesh, Iceberg, Flyte).
• Partner closely with People Analysts, HR Business Partners, and other stakeholders to understand their analytical needs and translate them into robust data solutions, providing well-structured, documented, and reliable datasets.
• Implement and monitor data quality checks, identify discrepancies, troubleshoot data issues, and ensure the reliability and integrity of people data across all systems.
• Continuously monitor the performance of data pipelines and models, identifying bottlenecks and implementing solutions to ensure the efficiency and scalability of our people data infrastructure.
• Create and maintain comprehensive documentation for data pipelines, models, and processes, and champion data engineering best practices (e.g., version control, testing, CI/CD) within the team.
• Implement and enforce strict data security measures and ensure all data handling practices comply with internal policies and external regulations (e.g., GDPR, CCPA) related to employee data privacy.
• Collaborate with broader enterprise analytics and data engineering teams to align on data architecture standards, integrate people data with other business domains, and contribute to the overall evolution of the company's data platform.

REQUIRED QUALIFICATIONS

• 5-7 years of progressive experience in Data Engineering, Analytics Engineering, or a similar role focused on building and optimizing data pipelines and data infrastructure.
• Expert-level proficiency in SQL for complex data manipulation and querying, and advanced Python for scripting, data processing, and automation.
• Extensive experience with cloud-based data warehousing solutions (e.g., Snowflake, Google BigQuery, AWS Redshift, Databricks/Delta Lake) and data lake technologies (e.g., AWS S3, Azure Data Lake Storage).
• Deep understanding and proven experience in designing, implementing, and maintaining robust data models (e.g., dimensional modeling, Kimball methodology) for analytical purposes.
• Hands-on experience building and optimizing complex ETL/ELT processes and data pipelines using modern tools such as dbt, Apache Airflow, Flyte, Dagster, or similar orchestration platforms.
• Excellent communication skills, both written and verbal, with the ability to translate technical concepts for non-technical stakeholders and collaborate effectively across diverse teams.
• Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related field.
• Strong technical foundation in data engineering, with expertise in SQL, Python, and experience working with cloud-based data platforms (e.g., AWS, GCP, Azure).

Preferred Qualifications:

• Experience with big data processing frameworks (e.g., Apache Spark, Flink) and advanced data warehousing features like schema evolution or time travel (e.g., Apache Iceberg, Delta Lake).
• Direct hands-on experience with tools mentioned in our stack like Palantir Foundry, SQLMesh, Flyte, or similar cutting-edge data orchestration and transformation platforms.
• Relevant professional certifications from major cloud providers (e.g., AWS Certified Data Analytics - Specialty, Google Cloud Professional Data Engineer).
• Experience with tools like Terraform, CloudFormation, Docker, or Kubernetes for managing data infrastructure and deploying applications.
• Familiarity with integrating data pipelines with leading BI tools (e.g., Tableau, Power BI, Looker) to optimize dashboard performance and data accessibility for end-users.
• Deep expertise working with data from specific enterprise HRIS systems like Rippling, Workday, and Oracle HCM Cloud including their data models and APIs.
• Solid understanding of HR data concepts, metrics, and common HR systems (HRIS, ATS, LMS), with a strong interest in People Analytics.

Position Overview:

We’re looking for a Senior Data Implementation Engineer to help lead and accelerate Acadian’s data transformation efforts. This role is central to reimagining and rebuilding our data platform so that we can continue to scale our investment research, production data workflows, and operational capabilities in a rapidly evolving landscape.

You’ll be hands-on in designing and implementing modern data pipelines and platform capabilities using tools such as Apache Arrow, Polars, Dagster, and other emerging technologies. You’ll also help shape how we use AI-assisted development tools, such as Claude & ChatGPT, and related automation approaches, to improve the speed, quality, and maintainability of data pipeline development.

This is a senior engineering role for someone who can operate across both strategy and execution: deeply understanding existing systems, designing pragmatic transition paths, mentoring others, and building reliable, scalable, high-performance data infrastructure that directly supports Acadian’s quantitative investment process.

Acadian supports a hybrid work environment; employees are on-site in the Boston office 3 days a week.

What You’ll Do

• Lead Data Pipeline Design and Implementation
  • Design, build, and optimize robust data pipelines that are reliable, scalable, observable, and performant. Use modern data engineering tools and patterns to support investment research, production workflows, and downstream analytics.
• Drive System Modernization
  • Deeply understand existing data systems, identify architectural limitations, and lead the transition toward more modern, maintainable, and efficient data architectures.
• Shape Technical Direction
  • Partner with Data Platform Engineering, Data Analysis, and investment teams to define implementation patterns, pipeline standards, and architectural approaches that can scale across the organization.
• Apply AI Tooling to Engineering Workflows
  • Use AI-assisted development tools such as Claude, ChatGPT, or similar technologies to accelerate pipeline development, automate repetitive workflows, improve documentation, generate tests, and enhance developer productivity.
• Mentor and Lead Others
  • Provide technical guidance to junior and mid-level engineers. Help raise the engineering bar through code reviews, design discussions, pairing, documentation, and thoughtful mentorship. Lead small project teams or workstreams when needed.
• Improve Reliability and Operational Excellence
  • Develop systems with strong testing, monitoring, lineage, data quality checks, and operational supportability. Help ensure that data pipelines are resilient, transparent, and easy to maintain.
• Collaborate Across Teams
  • Work closely with stakeholders across technology, data, research, and investment teams to understand requirements, resolve ambiguity, and deliver solutions that create measurable business value.
• Continuously Improve the Platform
  • Identify opportunities to simplify, automate, and improve the data ecosystem. Champion practical innovation while balancing speed, stability, maintainability, and long-term architectural quality.

We’re Looking for Teammates With

• Bachelor’s degree in a relevant field

• A strong background in data engineering, data platform implementation, or data-intensive software engineering, ideally with 5+ years of experience in a financial, investment, or technology-driven environment.
• Deep experience building production-grade data pipelines, including ingestion, transformation, validation, orchestration, monitoring, and deployment.
• Strong Python programming skills, with relevant experience working with Dagster, Airflow or similar technologies.
• Experience with modern data architecture patterns such as data lakehouse, Medallion architecture, composable data systems, microservices, and event-driven or workflow-oriented architectures.
• Familiarity with Data Mesh concepts, including decentralized data ownership, domain-oriented data products, governance, and discoverability.
• Experience with orchestration, workflow, and data transformation tools such as dbt, Polars, Pandas, Apache Arrow, or comparable technologies.
• Practical experience using AI-assisted development tools such as Claude, ChatGPT, GitHub Copilot, or similar platforms to support data pipeline development, automation, testing, documentation, and engineering productivity.
• Demonstrated ability to mentor engineers, lead technical workstreams, and influence engineering practices across a team.
• A strong sense of ownership and the ability to operate effectively in ambiguous environments, balancing tactical delivery with long-term architectural thinking.
• A passion for building systems that are efficient, scalable, reliable, observable, and easy for others to use.
• An understanding of how data infrastructure supports the broader investment process, and a desire to deliver solutions that improve outcomes for clients and create meaningful business impact.

The base salary range for this role is $175,000 – $200,000 per year. Actual compensation will be determined based on a candidate’s skills, qualifications, and relevant experience. In addition to base pay, this position may be eligible for discretionary incentive compensation and includes participation in Acadian’s comprehensive benefits program, which includes health, retirement, and wellness offerings.

We are looking for a Senior Clinical Data Engineer to join our Research Operations team, which drives the end-to-end execution of studies that drive new feature development and validation. You’ll manage the data that forms the foundation of our clinical trials—ensuring that data pipelines are fast, transparent, and comply with all applicable regulations. 

In this role, you’ll work at the frontier of R&D health sensing and gain hands-on experience with every part of the clinical database lifecycle: from design and validation through database lock and archival. Working closely with our regulatory, software, and science teams, you’ll raise the bar for how Oura runs clinical studies. 

This is a remote US role with a preference for candidates based on the East coast.

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’ll Do:

• Oversee the clinical data lifecycle for a portfolio of studies: ensuring timelines and data integrity are maintained.
• Work closely with research project managers, algorithm developers, data engineers, and external vendors to align data management activities with broader trial objectives.
• Identify data-related risks and implement mitigation strategies, while driving process improvements and adopting emerging technologies as needed.
• Lead data quality management, query analysis, optimization, deduplication, auditing, and validation of regulated data ecosystems.
• Develop and oversee the design of Case Report Forms (CRFs) and electronic data capture (EDC) systems to ensure accurate, complete, and protocol-compliant data collection.
• Build the Clinical Data Repository that will form the backbone of all research and regulatory work at Oura

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

• 7+ years of industry experience with clinical data management
• Fluency with regulations such as ICH GCP, 21 CFR Part 11, and standards such as CDISC (SDTM/ADaM)
• Proficiency in an industry-standard EDC system (e.g. Medidata Rave, Castor, REDCap) and data engineering tools (e.g. AWS S3, Glue, Athena; Databricks; Dagster; dbt)
• Experience bridging clinical data management and research with enterprise practices and standards
• Comfort with building dashboards using tools like Tableau or Databricks
• Flexibility with scheduling to enable calls with global collaborators

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 $158,950 - $187,000
• Region 3 $147,900 - $174,000

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

We are not considering candidates residing in the following states: Alaska (AK), Delaware (DE), Iowa (IA), Mississippi (MS), Nebraska (NE), South Dakota (SD), West Virginia (WV), and Wisconsin (WI)

About the Company:

About the Role:

Octave is hiring Licensed Clinicians to work within an organizational structure to provide high-quality behavioral health care to our clients. Octave Clinicians will join a diverse clinical team in providing therapy in a collaborative, client-centered, evolving environment. Octave Clinicians demonstrate strong clinical service thinking, informed by evidence-informed therapies. It is our effort to drive quality access and customize mental health treatment plans and service delivery for our clients. You will treat adult clients with stress, depression, anxiety, or other mental health concerns via video, using short-term, evidence-based therapies. Candidates must be licensed in Massachusetts at the master's or doctoral level, well-versed in evidence-based therapies, like CBT or DBT, and an expert in the delivery of culturally responsive care.

We are seeking out Licensed Clinicians who are willing to work both in-person and remotely, but an office space is required to start. As a Clinician with Octave, you will be able to build out a caseload that gives your clients the option to see you virtually or in-person. It is required that you already have a commercial clinical space that you can operate from to see clients in person through Octave. At this time, we are not providing an office space within the state of Massachusetts. You can see clients in person from your home office or commercial office setting.

You are a good fit if you are:

• Passionate about expanding access to high-quality mental health care.
• Skilled at providing short-term, evidence-based therapies to a diverse range of adult clients.
• Adept at building and maintaining a strong therapeutic alliance.
• Willing to deliver feedback-informed care using standardized measures.
• Expert at providing culturally responsive and culturally competent care.
• Open to learning from your colleagues and clients.
• Someone who thrives in a culture of thoughtful feedback and transparency.

Qualifications:

• LMFT, LMHC, LICSW, PsyD
• Current License in Massachusetts.
• Master’s Degree or higher from an accredited school of Social Work, Psychology, Counseling or Marriage, or Family and Child Counseling.
• Must be licensed and in good standing with your governing licensing board.
• Preferred 1 year of related post-license clinical experience required. 
• At least 2 years of experience preferred in one or more of the following areas:
  • CBT for depression or anxiety
  • DBT for emotion dysregulation
  • Unified Protocol for emotional distress
  • CPT or PE for trauma
• Must have your own office space and are willing to provide in-person services in the Boston area.

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 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: 

• 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)

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: 
$83-$133/hour for master's level license.
$93-$143/hour for doctoral level license.

Virtual Rates: 
$80-$130/hour for master's level license.
$90-$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.

Role overview

As a core member of the Data Systems Analysis team, the Associate Business Systems Analyst will support the design, implementation, and ongoing improvement of our data and analytics systems. This role acts as a bridge between business stakeholders and the technical capabilities of our modern data stack, with a growing focus on how we can responsibly apply AI to improve our work. The ideal candidate is eager to learn and explore the new technology in the industry and comfortable working under the guidance of senior analysts and managers to translate business needs into clear, actionable requirements. You will collaborate closely with cross-functional partners across Data, Engineering, and Business teams to document processes, support solution design, and help ensure our systems effectively enable day-to-day operations and strategic decision making.

What you'll do

• Requirements design & analysis: Support requirements gathering sessions with business stakeholders, capturing notes and translating them into clear user stories under the guidance of senior team members. Partner with senior analysts and Technical Product Managers to refine requirements into implementable work items for the engineering team.
• Documentation: Help document current-state and future-state business processes, identifying gaps and pain points. Maintain and update document templates, runbooks, and training materials so that documentation stays current as systems and processes evolve.
• System & data analysis: Assist in analyzing data and system behavior to understand how existing solutions support business processes. Help identify opportunities for process or system improvements, documenting findings and recommendations for review by senior team members.
• Implementation support: Collaborating with engineers, data teams, and product partners to deliver changes that meet documented requirements. Assist with coordination between business stakeholders and technical teams to clarify questions and resolve minor issues.
• AI exploration & enablement: Explore and evaluate AI (ideally build as well) tools and techniques that can improve operational processes, efficiency tracking, and coding workflows for data teams. Prototype small, low-risk AI-assisted workflows and document learnings, limitations, and best practices for the team. Partner with senior analysts and engineers to translate AI opportunities into concrete requirements or proof-of-concept implementations.
• User Acceptance Testing: Help define and document UAT scenarios and test cases based on requirements and business workflows. Coordinate with business stakeholders to schedule UAT, track feedback, and record defects and retest results. 
• Continuous improvement: Support process and documentation clean-up efforts, including template standardization, version control, and knowledge management. Stay up to date on industry concepts and best practices in business analysis and data/analytics and bring relevant ideas back to the team.

What you'll bring

• 1+ year of experience as a Business Analyst, Business Systems Analyst, Data Analyst, or similar role, or relevant internship/project experience.
• A self-starter mindset with strong curiosity and willingness to learn from feedback and mentorship.
• Analytical problem-solving skills and comfort breaking larger problems into smaller, structured questions.
• Ability to navigate ambiguity by asking clarifying questions, documenting assumptions, and escalating risks appropriately.
• Strong written and verbal communication skills, including:
  • Clear meeting notes and action items
  • Well-structured requirements and documentation
  • Ability to explain findings in straightforward business language
• A collaborative working style, with the ability to partner across business, data, and engineering teams and incorporate input from multiple stakeholders.
• Experience or exposure to tools like Snowflake, dbt, and BI platforms
• Experience using related AI tools such as Cursor, Claude, Glean. 
• Strong SQL skills.
• Comfort working in an Agile / scrum environment (e.g., user stories, backlogs, sprints) or a strong desire to learn.
• Business outcome oriented.

Role overview

CarGurus is looking for a Senior Product Data Analyst to navigate our rich data environment and drive impactful outcomes for our customers and business. You will work cross-functionally to build out core data assets, empower go-to-market teams with revenue-generating insights, and design novel metrics to guide the business.

Our Product Data Analytics org supports our Product, Engineering, and Go-to-Market teams, providing the final word on all analytics for CarGurus’ user and dealer experiences. We are looking for thoughtful, curious, internally driven analytics experts who don’t just pull data, but draw the “so-what” from complex, disparate sources and present these findings to internal teams and external customers. 

What you’ll do

• Communicate and present complex quantitative findings in easily digestible terms to company leadership, homing in on key takeaways. Concretely and informatively respond to any probing, on-the-spot follow-up questions from senior decision-makers. 
• Conduct exploratory empirical analyses using SQL and Python that bridge disparate data sources (e.g. website usage, subscription records, inventory volumes, etc.). to quantify product performance, user behavior, and/or market trends. Distill unstructured “big data” around product performance, user behavior and/or market insights into actionable outcomes .  
• Architect new data assets and production-grade data marts via dbt. You will fix modeling bottlenecks and optimize data lineages to ensure scalability. 
• Serve as the technical expert in high-stakes Go-To-Market engagements. You will partner with Sales and Customer Success teams to provide data-backed proof of value, participating in client-facing meetings to help secure new partnerships or mitigate churn through sophisticated impact analysis.
• Partner with engineering teams to advance the company’s core data modeling and architecture, with a focus on optimizing, integrating, and distilling large raw datasets and metadata. 
• Creatively compress sweeping, high-level exploratory requests into specific calculations that address your stakeholders’ underlying needs. Relentlessly dig into the data, taking initiative to consult with other individuals and teams as you judge necessary. Go beyond the letter of the initial assignment, to hammer out any anomalies or self-direct your inquiry into other relevant issues. 
• Use Claude Code and agentic workflows to automate,  to automate, accelerate, and escalate the sophistication of your analyses.
• Advocate for specific, data-driven product innovations that help further company strategy, primarily in partnership with the Product, Engineering, and Go-to-Market teams. 
• Lead rigorous, holistic discussions about statistical analyses and ML modeling.  You are expected to be a “self-teaching” practitioner, staying ahead of quantitative trends. 
• Participate in brainstorming and planning discussions across the organization to these ends. Avoid passivity in the face of flawed proposals; tactfully and persuasively push back against potential missteps.
• Craft the metrics that define success. You’ll condense abstract or loosely-defined concepts into concrete calculations and relentlessly audit existing metrics for accuracy and relevance.
• Build intuitive dashboards and other visual monitoring tools to guide stakeholders’ daily decision-making. Experiment with new kinds of visualizations that you believe could be better utilized in the organization. Re-work underlying code to appropriately structure visualization inputs.

What you’ll bring

• 4+ years of experience in analytics, with a proven track record of influencing business strategy through complex modeling and applied statistics. 
• Expert-level skills in SQL, Python, and dbt. Proficiency with file manipulation and version control via Git.
• Strong data storytelling and communication skills, presenting findings to senior leadership and external customers.
• Hands-on experience leveraging  GenAI  to optimize workflows and agentic coding platforms like Claude Code (preferred) or Cursor.
• A self-starter who can take a “grassroots” idea, scope its viability, and bring it to scale.
• A natural thought leader with a proven track record of influencing business strategy.
• Strong command of hypothesis testing, parametric/nonparametric regression, and building automated statistical pipelines. 
• Knack for creative data visualizations.
• You are a “tactful challenger”. You don’t just accept flawed proposals; you provide data-backed alternatives and can explain p-values to executives as easily as you explain them to technical audiences. 
• Strong project planning skills, with experience building roadmaps, estimating required resources, and flagging interdependencies with other teams.
• Preferred tools/programs: Snowflake, Claude Code, Cursor, Looker/LookML, Omni.

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.

About Toast

At Toast, we're building the restaurant platform that helps restaurants adapt, take control, and thrive. The Customer Success (CS) organization plays a pivotal role in helping customers get the most out of our products and we're transforming our data capabilities to drive a new era of proactive, data-informed customer engagement. 

The CS Data & Analytics team is at the center of this transformation, building the data infrastructure that makes proactive, data-informed customer engagement possible at scale for ~150,000 restaurant locations.

Role Overview

You'll be a founding member of a newly chartered data engineering function within Customer Success, with a direct hand in shaping the architecture, tooling, domain model, and team culture from day one. This is a rare opportunity to build something from greenfield, with visibility to VP and senior CS leadership.

As an Analytics Engineer on the Customer Success Data & Analytics team, you'll bridge the gap between raw data and business-ready insights. You'll build the semantic layer, dbt models, and analytics datasets that power reporting, dashboards, and AI-driven workflows across the CS organization. Reporting to the Director of Data Infrastructure & Engineering, you'll work closely with Data Engineers, Analysts, and CS operations leaders to ensure data is not just available but trusted, consistent, and decision-ready.

This is a hands-on role focused on data modeling, metrics standardization, and analytics infrastructure, with a direct line to CS outcomes like customer retention, agent performance, and proactive customer engagement.

A day in the life (Responsibilities) 

• Build and maintain dbt models that transform raw CS source data from systems like Salesforce, Five9, Intercom, NICE, and LevelAI into clean, analytics-ready datasets across CS domains including Customer 360, case management, omnichannel interactions, and agent performance.
• Design and own the semantic and metrics layer for CS KPIs including customer health scores, CSAT, case resolution rates, handle time, and retention signals, ensuring consistent definitions across all reporting surfaces.
• Partner with CS analysts and business stakeholders to translate reporting requirements into reusable, well-documented data models.
• Implement data testing, validation, and observability frameworks to ensure CS data assets are reliable and trustworthy at all times.
• Contribute to self-service analytics enablement by building datasets and semantic models in Snowflake and Sigma/Hex that allow CS analysts and operations leaders to answer questions independently.
• Collaborate with Data Engineers to ensure upstream pipeline outputs are modeled correctly and fit for purpose for analytics use cases.
• Establish and enforce data modeling standards, naming conventions, and documentation practices that scale as the team grows.
• Support AI and ML use cases by building clean, structured feature datasets for customer health scoring, churn prediction, and agent assist models.
• Participate in code reviews, design discussions, and technical architecture planning to continually raise engineering standards.
• Ensure data pipelines, storage, and access patterns adhere to BTT data standards, security policies, and compliance requirements.
• Partner with BTT governance forums to align on design patterns, data architecture decisions, and SLA expectations.
• Maintain documentation and metadata for all data assets in accordance with established governance processes.

What you'll need to thrive (Requirements)

• 5 or more years of experience in analytics engineering, data engineering, or a related role with a strong focus on data modeling and analytics infrastructure.
• Expert-level SQL and hands-on experience with dbt for building and maintaining data models in a cloud data warehouse environment.
• Experience with Snowflake or a comparable modern data warehouse such as BigQuery or Redshift.
• Familiarity with BI and analytics tools such as Sigma, Hex, Tableau, or Looker.
• Strong understanding of dimensional modeling, metrics layers, and analytics-ready dataset design.
• Ability to translate ambiguous business requirements from non-technical CS stakeholders into clean, scalable data models.
• Demonstrated ability to implement data quality testing, monitoring, and documentation as a standard practice.
• Demonstrated ability to communicate technical tradeoffs and data concepts clearly to non-technical stakeholders, including operations and business leaders.
• Collaborative working style with experience partnering across Data Engineering, Analytics, and business operations teams.
• Strong problem-solving skills, attention to detail, and a drive to build scalable, reliable systems.
  
  

What will help you stand out (Non-essential Skills/Nice to Haves)

• Experience building and optimizing data systems that operate at significant scale, managing billions of records across multiple data domains and systems, with the foresight to design for 5-year growth and future platform scale.
• Comfort using AI-assisted development tools such as GitHub Copilot, Claude, or Snowflake Cortex AI to accelerate pipeline development, testing, and documentation, and curiosity about how GenAI can enhance data product quality.
• Experience with real-time data streaming using Kafka, Kinesis, or Pub/Sub.
• Exposure to data governance, metadata management, and observability tools.
• Background in SaaS or Customer Success analytics, including usage data, retention metrics, and customer health.
• Experience working with CS or CX-adjacent source systems such as Salesforce, Five9, Intercom, or similar CCaaS/CRM platforms, and familiarity with the data models and integration patterns they produce.
• Knowledge of data security, privacy, and compliance in cloud environments.
• Experience with CI/CD, infrastructure as code, or containerization such as Docker or Terraform.

AI at Toast

At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.

Our Total Rewards Philosophy

We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits.

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.

About Toast

At Toast, we're building the restaurant platform that helps restaurants adapt, take control, and thrive. The Customer Success (CS) organization plays a pivotal role in helping customers get the most out of our products and we're transforming our data capabilities to drive a new era of proactive, data-informed customer engagement. 

The CS Data & Analytics team is at the center of this transformation, building the data infrastructure that makes proactive, data-informed customer engagement possible at scale for ~150,000 restaurant locations.

Role Overview

You'll be a founding member of a newly chartered data engineering function within Customer Success with a direct hand in shaping the architecture, tooling, domain model, and team culture from day one. This is a rare opportunity to build something from greenfield, with visibility to VP and senior CS leadership.

As a Data Engineer on the Customer Success Data & Analytics team, you’ll design, build, and scale the data platforms and pipelines that power insights across our Customer Success organization. Reporting to the Director of Data Infrastructure & Engineering, you’ll work closely with Data Infrastructure, Analytics, Engineering, and Product teams to ensure high-quality, reliable data is accessible and actionable.

This is a hands-on engineering role focused on delivering production-grade, scalable, and reliable data systems that support analytics, reporting, and operational decision-making across Customer Success.

A day in the life (Responsibilities) 

• Design, build, and maintain scalable, production-ready data pipelines (batch and real-time) for Customer Success using modern data technologies.
• Develop and evolve data models and schemas that enable reporting, analytics, and machine learning across Customer Success functions.
• Partner with CS operations leaders across care, workforce, and CX to translate business requirements into pipeline and infrastructure priorities, ensuring data work is directly connected to customer retention and operational outcomes.
• Ensure data quality, observability, lineage, and governance are built into every data workflow.
• Implement best practices for cost-efficient, performant cloud-based data infrastructure.
• Contribute to data platform tooling and frameworks that support self-service analytics and reusability across teams.
• Actively monitor and optimize data workflows for reliability, scale, and maintainability.
• Collaborate with cross-functional stakeholders to define and track key metrics and ensure consistent data definitions.
• Participate in code reviews, design discussions, and technical architecture planning to continually raise engineering standards.
• Build and maintain semantic and metrics layer foundations (e.g., dbt metrics, Snowflake semantic layer) that enable consistent, reusable definitions of CS KPIs across reporting and analytics surfaces.
• Contribute to the design and evolution of the CS data domain model including Customer 360, omnichannel interaction data, case management, and agent performance in close partnership with cross-functional stakeholders.
  
  

Governance & Standards

Partner with Toast's Business, Technology & Transformation (BTT) organization to establish data governance practices, security policies, and architecture standards that will serve as the foundation for CS data infrastructure. As a founding team member, you'll help define the norms, not just follow them.

• Ensure data pipelines, storage, and access patterns adhere to BTT data standards, security policies, and compliance requirements.
• Partner with BTT governance forums to align on design patterns, data architecture decisions, and SLA expectations.
• Maintain documentation and metadata for all data assets in accordance with established governance processes.
  
  

What you'll need to thrive (Requirements)

• 5+ years of experience in data engineering, software engineering, or a related role building and maintaining data systems in production.
• Strong SQL skills and experience with a programming language such as Python, Scala, or Java.
• Hands-on experience with cloud-based data platforms (AWS, GCP, or Azure).
• Experience building and optimizing ETL/ELT workflows using tools such as Airflow, dbt, or similar orchestration frameworks.
• Deep understanding of data modeling, data warehousing, and pipeline design.
• Ability to write clean, maintainable, and well-tested code for data transformations and automation.
• Demonstrated ability to communicate technical tradeoffs and data concepts clearly to non-technical stakeholders, including operations and business leaders.
• Proven ability to work collaboratively with analytics, operations, and engineering teams.
• Strong problem-solving skills, attention to detail, and a drive to build scalable, reliable systems.
  
  

What will help you stand out (Non-essential Skills/Nice to Haves)

• Experience building and optimizing data systems that operate at significant scale, managing billions of records across multiple data domains and systems, with the foresight to design for 5-year growth and future platform scale.
• Comfort using AI-assisted development tools (e.g., GitHub Copilot, Claude, Snowflake Cortex AI) to accelerate pipeline development, testing, and documentation and curiosity about how GenAI can enhance data product quality.
• Experience with real-time data streaming (Kafka, Kinesis, or Pub/Sub).
• Familiarity with modern data warehouse/lakehouse technologies (e.g., Snowflake, BigQuery, Redshift, Delta Lake).
• Experience with CI/CD, infrastructure as code, or containerization (e.g., Docker, Terraform).
• Exposure to data governance, metadata management, and observability tools.
• Background in SaaS or Customer Success analytics (usage data, retention metrics, customer health).
• Experience working with CS or CX-adjacent source systems such as Salesforce, Five9, Intercom, or similar CCaaS/CRM platforms and familiarity with the data models and integration patterns they produce.
• Knowledge of data security, privacy, and compliance in cloud environments.

AI at Toast

At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.

Our Total Rewards Philosophy

We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits.