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.

The Role

Fusion is a global electronics distributor building a modern data platform on top of a 20 year old legacy system. Palantir Foundry is a core part of that stack. We use it to deliver unified views of our customers, products, and operations, and we are leaning hard into AIP and agent driven workflows to compress delivery cycles. We are looking for a senior engineer who can work hands on in Foundry: building pipelines, ontology models, agents, and applications alongside our external Palantir consultants today, and progressively taking ownership as the platform matures.

This is an execution focused role with real autonomy. You will design and build solutions, work directly with business stakeholders, and have a voice in how the platform evolves.

What You’ll Do

• Build and maintain data pipelines: Design, build, and optimize batch and streaming pipelines in Foundry (Pipeline Builder with AI assist, Code Repositories, Code Workbook) that ingest data from internal and external source systems.
• Develop ontology models: Create and maintain ontology objects, relationships, link types, functions, and actions that represent business domains and power downstream applications, agents, and AIP workflows.
• Build AI native experiences with AIP: Use AIP Logic to compose AI driven functions, AIP Agent Studio to stand up domain agents, and AIP Threads or AIP Assist to embed conversational interfaces into Workshop apps. Use AIP Evals to validate prompt and agent behavior before promotion to production.
• Leverage AIP Analyst and FDE AI for delivery acceleration: Use AIP Analyst for ad hoc analytical work directly against the ontology, and lean on FDE AI and Solution Designer to scaffold ontology objects, pipelines, and starter applications. Refine the AI generated output, harden it for production, and bring it under proper change control.
• Deliver Foundry applications: Build and iterate on Workshop, Quiver, and Contour applications that give sales, operations, and leadership actionable views of the business, including embedded agentic actions and write back through Ontology Actions.
• Work alongside external consultants: Collaborate closely with our Palantir implementation partners to understand existing designs, validate data flows, and progressively take on more of the day to day Foundry work.
• Connect Foundry to the broader data platform: As the data modernization team builds out medallion layer domain stores and DBT based pipelines on Azure, help align Foundry’s ontology and ingestion paths so the platform can transition from legacy sources to the new architecture cleanly.
• Integrate with external systems: Build and maintain OSDK based integrations and APIs that connect Foundry to internal services and third party platforms.
• Engage stakeholders directly: Gather requirements from business teams, demo solutions, train end users, and incorporate feedback into iterations.
• Ensure data quality and observability: Implement validation, lineage tracking, health checks, and monitoring within Foundry pipelines. Build automated data quality checks, including AIP assisted ones, to keep data accurate and reliable.
• Apply AI tooling to your own workflow: Use AIP and AI assisted development practices to accelerate pipeline work, code review, and documentation.

What You Bring

Data Engineering Foundations

These are the bedrock skills we expect every senior data engineer on this team to have, regardless of platform. Foundry is the surface; these are the muscles underneath.

• 7+ years building production data pipelines in modern stacks.
• Strong SQL, including window functions, CTEs, set operations, and query optimization on large datasets. You can read an execution plan and explain the cost.
• Solid grasp of dimensional modeling (Kimball, star schema), Data Vault, or equivalent. You can defend a modeling choice with reasoning, not dogma.
• Experience designing for slowly changing dimensions, idempotent loads, late arriving data, backfills, and incremental processing.
• Proficiency in Python, including PySpark and pandas for transformation work.
• Working knowledge of the Spark execution model: partitioning, shuffles, skew, broadcast joins, and tuning.
• Experience with ETL and ELT patterns, CDC pipelines, and workflow orchestration.
• Familiarity with modern transformation frameworks such as DBT, including tests, documentation, snapshots, and incremental materializations.
• Understanding of columnar formats (Parquet, Delta, Iceberg), table formats, and partitioning strategies.
• Practical experience implementing data quality frameworks, data contracts, and SLAs between producers and consumers.
• Comfort with Git workflows, code review, branching strategies, and CI/CD for data platforms.

Palantir Foundry and AIP

• 2+ years hands on Palantir Foundry experience with delivered work you can describe in detail.
• Strong knowledge of core Foundry capabilities: Ontology Manager, Ontology Functions and Actions, Pipeline Builder, Code Repositories, Code Workbook, Workshop, Builds, and platform security (branching, resource allocation, rollbacks).
• Experience with Foundry analytical and integration tools: Quiver, Contour, OSDK, and Marketplace products.
• Hands on experience with AIP for augmenting data workflows: AIP Logic, AIP Assist and Threads, and AIP assisted Pipeline Builder.
• Familiarity with newer AIP surfaces is a strong plus: AIP Agent Studio, AIP Analyst, FDE AI and Solution Designer, AIP Evals, and Automate.
• Understanding of how to evaluate, guardrail, and version AI driven functions and agents in a production context.

Cloud, Architecture, and Stakeholder Skills

• Proficiency with Azure data services, or equivalent depth in AWS or GCP with willingness to work in Azure.
• Solid understanding of data modeling across relational, analytical, and document paradigms.
• Ability to translate business requirements into technical designs and explain trade offs to non technical stakeholders.
• Self directed work style. You identify what needs doing and get it done without waiting for direction.

Nice to Have

• Production experience with DBT on Azure (Synapse, Fabric, or Databricks).
• Experience with medallion architecture (Bronze, Silver, Gold) or lakehouse patterns on Databricks, Snowflake, or equivalent.
• Experience evaluating LLM powered features: prompt design, eval harnesses, hallucination guardrails, and cost or latency tuning.
• Experience building or operating real time pricing, recommendation, or scoring engines.
• Familiarity with electronics distribution, supply chain, or ERP data, particularly Dynamics GP and Great Plains.
• Background in domain driven design or microservices architecture.
• TypeScript or C# for OSDK consumers and downstream services.
• Exposure to CRM, payment, or logistics API integrations.

Why Fusion

• Greenfield meets legacy: You’ll help build a modern data platform while keeping a 20 year business running. Real engineering problems, not toy projects.
• Real impact, small team: A lean team where your work is visible and your judgment matters. No layers of approval.
• AI first engineering culture: We use AIP and AI tooling aggressively. One engineer with the right tools does the work of three.
• Global scale: Fusion operates across the US, Singapore, and Hong Kong. The platform serves users worldwide.

Position Summary

We are seeking a Quantitative Analyst with three or more years of experience to help advance our research and investment analytics capabilities, with a particular emphasis on data science. This highly capable person will build and maintain a thoughtful data layer across portfolios, managers, and asset classes, while developing analytical tools and workflows that improve decision-making and efficiency. This is a hybrid role that blends investment domain knowledge with data science and applied AI, offering an opportunity to help shape the evolution of our investment platform.

This role is located in Boston, MA.

Primary Responsibilities

• Partner with senior investment professionals and other colleagues to translate strategic priorities into practical analytical solutions, workflow enhancements, and automation opportunities.
• Support the Investment team in monitoring existing investments and underwriting new opportunities, including internal discussions related to portfolio construction, performance, and risk.
• Build and maintain scalable data pipelines and workflows to support reliable data ingestion, transformation, and quality across investment, portfolio, and client datasets.
• Help develop and enhance the firm’s investment data layer, with a focus on making information more structured, accessible, and actionable across asset classes and investment processes.
• Analyze large and complex datasets to identify trends, behavioral patterns, performance drivers, and other insights that can inform investment decision-making.
• Design, validate, and operationalize analytical models and AI-enabled tools to support areas such as asset allocation, manager research, portfolio monitoring, and risk assessment.
• Maintain strong documentation and adhere to appropriate data governance, control, and compliance standards consistent with industry best practices.

Qualifications

Required

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Finance, or a quantitative field.
• Three or more years of relevant experience in data science, analytics, investment research, or a related role, ideally within financial services, asset management, or wealth management.
• Experience using Git and GitHub for version control, collaborative development, code reviews, and managing production-grade analytical or quantitative applications.
• Strong programming skills with experience building analytical workflows, models, and data pipelines.
• Experience working with modern data infrastructure, including Snowflake or similar environments.
• Solid understanding of financial markets, portfolio construction, investment performance, and risk concepts.
• Excellent communication skills and the ability to explain complex analyses and technical concepts clearly.

Preferred

• Experience with business intelligence and data visualization platforms such as Power BI.
• Familiarity with cloud environments such as AWS, Azure, or GCP.
• Experience applying machine learning or AI tools in production or business workflows.
• Experience with agentic workflows using tools such as Copilot Studio or Claude.
• Familiarity with modern LLM ecosystems and related concepts such as retrieval-augmented generation (RAG), vector databases, agent frameworks, or tool/function calling.
• Exposure to investment manager research, underwriting, or portfolio analytics is a plus.

This position is an exempt position. The annualized base pay range for this role is expected to be between $150,000–$250,000 base salary compensation range. Actual base pay may vary based on factors including, but not limited to, experience, subject matter expertise, geographic location where work will be performed, and the applicant’s skill set. The base pay is just one component of the total compensation package. Other rewards may include an annual cash bonus and a comprehensive benefits package, including but not limited to medical, dental, vision, life insurance, and 401(k). Please note that the job title is subject to change based on the selected candidate’s experience and education.

SCS is a fiduciary wealth and investment management firm serving some of the world’s most ambitious wealth creators — ultra‑high‑net‑worth individuals, families, and institutions. SCS exists to transform wealth into possibility, helping its clients protect and amplify their capital, align it with their purpose, and build multi‑generational legacies.

SCS delivers an integrated family office and investment platform that brings together sophisticated multi‑asset class portfolio management, access to private investments, holistic wealth and estate planning, next‑generation education, and purpose‑driven advisory. SCS’s team is supported by a modern, scalable technology infrastructure that enhances SCS’s ability to deliver a seamless, proactive, white‑glove client experience.

SCS is part of Focus Financial Partners, a leading financial services firm comprised of wealth management, family office, and business management services.

#LI-SF1

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: 

• $166,400 - $208,000

*This is a remote role

#LI-Remote

Why This Role, Why Now

GTM Data Strategy & Operations stood up from scratch with no predecessor. Today the function runs on three offshore contractors and zero FTEs, managed by a single leader who is simultaneously building the agentic infrastructure, operating it in production, and driving major initiatives (hierarchy redesign, data quality assessment, vendor optimization).

The operating model is deliberately agentic AI–first: a multi-agent pipeline (Cartographer, Sentinel, Resolver, Reporting) handles detection, enrichment, hierarchy mapping, and conflict resolution at scale. This is not a future-state vision, these agents are live and processing enterprise account families in production today.

The problem: one person cannot build, operate, and extend this system while also managing strategic workstreams. The function currently covers only core Tier‑1 fields. Dozens of account, contact, and lead signals remain unaddressed. Every pipeline run, every failure diagnosis, and every offshore handoff flows through a single point of failure.

This role is the first onshore execution hire for an agent operator who can keep the system running, improve it, and extend detection and resolution coverage as GTM leadership prioritizes new data elements.

Role Summary

Sit between AI systems and GTM data. Operate, tune, and extend our agentic data quality pipeline (detection, enrichment, hierarchy mapping, conflict resolution) so it runs reliably, improves continuously, and expands to cover more of the data landscape. Own the handoff between automated output and human review, managing quality and throughput with our offshore team. You don’t build agents from scratch, but you run them, evaluate their output with GTM data judgment, and make them better.

Core Responsibilities

Agent Pipeline Operations

• Run and monitor production pipeline sessions (Cartographer, Sentinel, Resolver) across scheduled cadences; diagnose and resolve failures (API errors, session timeouts, data anomalies) without escalating to the function lead.
• Execute pipeline runs in Claude Claude and tmux; manage long-running batch processes; interpret logs and output to confirm data integrity before downstream handoff.
• Maintain pipeline orchestration scripts and configuration; extend agent coverage as new data elements are prioritized by GTM leadership.

Agent Tuning & Improvement

• Refine detection rules, prompt logic, and confidence thresholds based on output analysis and false-positive/negative patterns.
• Evaluate agent accuracy by segment (Enterprise vs. MM/SMB) and recommend rule or workflow changes backed by evidence.
• Run bake-offs (vendor vs. AI enrichment) to optimize cost, coverage, and accuracy; document results for decision-making.

Sentinel → Offshore Resolution Loop

• Own the handoff between Sentinel detection output and Concentrix triage queues; define queue structure, priority tiers, and resolution instructions.
• Monitor offshore resolution quality and throughput; refine detection rules based on patterns surfaced through triage.
• Close the feedback loop: track resolution outcomes back to agent configuration to reduce recurring false positives and improve detection precision.

Data Quality & Enrichment Operations

• Maintain ops-only staging fields; manage the promote-to-production flow with audit controls.
• Design and run AI-assisted enrichment workflows (Clay + LLM prompts) with evidence links and confidence thresholds.
• Monitor fill-rate, sampled accuracy, freshness, and cost-per-record by source and segment; surface vendor performance issues and recommend changes.
• Keep data dictionaries, SOPs, and runbooks current as agents and processes evolve.

Cross-Functional Partnership

• GTM Systems (SFDC): field configuration, permission sets, automation, flows.
• Data Engineering: source availability, ID mapping, lineage (no pipeline coding).
• Reporting: define metrics and acceptance criteria; partner on dashboard requirements.

What to Expect

This is a triage environment, not a steady-state one. The function is young, the data has known gaps, and the work is to stabilize and extend, not maintain and optimize. You’ll be building the plane while flying it, alongside a small team that operates with high autonomy and a bias toward measurable outcomes. If ambiguity and mess energize you, this is the right fit.

Success Metrics (6–12 Months)

Pipeline Reliability

• Scheduled pipeline runs execute without function-lead intervention; failure-to-resolution cycle time under 24 hours for non-blocking issues.
• Agent coverage extended to new data elements as prioritized (measured by number of signals under active detection).

Detection & Resolution Quality

• Sentinel detection precision and recall improve quarter over quarter, tracked by segment.
• Concentrix resolution queue throughput and accuracy meet defined acceptance thresholds.
• False-positive rate decreases through feedback-loop refinement.

Data Quality Outcomes

• Tier-1 field fill-rates: Country ≥95%; Vertical ≥90% at ≥85% sampled accuracy; Revenue bands ≥90%.
• Hierarchy coverage 65–80%+ across target segments.
• Enterprise cost-per-record reduction of 30–40% via AI-first + selective vendor usage.

Qualifications

Required

• 3–6 years in Data Ops, Sales Ops, or GTM Ops with hands-on data quality ownership for account and contact data.
• Proficiency with Snowflake (SQL for querying, analysis, validation) and SFDC (object model, field configuration, data flows).
• Working experience with Claude Code or comparable LLM-based tooling in an operational (not just experimental) context.
• Experience designing and running AI-assisted enrichment workflows (e.g., Clay + LLM prompts) and evaluating accuracy/coverage.
• Comfort operating in a command-line environment: tmux, shell scripts, log analysis, batch process monitoring.
• Process design mindset with a bias toward measurable outcomes; strong written communication.

Strong Plus

• Experience with account/contact data vendors (D&B, ZoomInfo, Clearbit, StoreLeads) and waterfall enrichment logic.
• Python for QA scripting, sampling, or light automation.
• Familiarity with prompt engineering, confidence scoring, and AI guardrails (evidence capture, versioned prompts, QA sampling gates).

Tool Stack

• Core: Snowflake (SQL), SFDC, Claude Code, Clay
• Pipeline: Shell orchestration, Cartographer / Sentinel / Resolver agents
• Enrichment: D&B, ZoomInfo, Clearbit, StoreLeads, LLM prompts
• Nice to Have: Python, SOQL, prompt engineering frameworks
• AI Guardrails (Expected Practice): Confidence floors, evidence capture, versioned prompts, 10% QA sampling gates, audit-on-promote, drift alerts, and privacy/compliance checks. This role is expected to uphold and improve these practices, not just follow them.

Why This Role, Why Now

GTM Data Strategy & Operations stood up from scratch with no predecessor. Today the function runs on three offshore contractors and zero FTEs, managed by a single leader who is simultaneously building the agentic infrastructure, operating it in production, and driving major initiatives (hierarchy redesign, data quality assessment, vendor optimization).

The operating model is deliberately agentic AI–first: a multi-agent pipeline (Cartographer, Sentinel, Resolver, Reporting) handles detection, enrichment, hierarchy mapping, and conflict resolution at scale. This is not a future-state vision, these agents are live and processing enterprise account families in production today.

The problem: one person cannot build, operate, and extend this system while also managing strategic workstreams. The function currently covers only core Tier‑1 fields. Dozens of account, contact, and lead signals remain unaddressed. Every pipeline run, every failure diagnosis, and every offshore handoff flows through a single point of failure.

This role is the first onshore execution hire for an agent operator who can keep the system running, improve it, and extend detection and resolution coverage as GTM leadership prioritizes new data elements.

Role Summary

Sit between AI systems and GTM data. Operate, tune, and extend our agentic data quality pipeline (detection, enrichment, hierarchy mapping, conflict resolution) so it runs reliably, improves continuously, and expands to cover more of the data landscape. Own the handoff between automated output and human review, managing quality and throughput with our offshore team. You don’t build agents from scratch, but you run them, evaluate their output with GTM data judgment, and make them better.

Core Responsibilities

Agent Pipeline Operations

• Run and monitor production pipeline sessions (Cartographer, Sentinel, Resolver) across scheduled cadences; diagnose and resolve failures (API errors, session timeouts, data anomalies) without escalating to the function lead.
• Execute pipeline runs in Claude Claude and tmux; manage long-running batch processes; interpret logs and output to confirm data integrity before downstream handoff.
• Maintain pipeline orchestration scripts and configuration; extend agent coverage as new data elements are prioritized by GTM leadership.

Agent Tuning & Improvement

• Refine detection rules, prompt logic, and confidence thresholds based on output analysis and false-positive/negative patterns.
• Evaluate agent accuracy by segment (Enterprise vs. MM/SMB) and recommend rule or workflow changes backed by evidence.
• Run bake-offs (vendor vs. AI enrichment) to optimize cost, coverage, and accuracy; document results for decision-making.

Sentinel → Offshore Resolution Loop

• Own the handoff between Sentinel detection output and Concentrix triage queues; define queue structure, priority tiers, and resolution instructions.
• Monitor offshore resolution quality and throughput; refine detection rules based on patterns surfaced through triage.
• Close the feedback loop: track resolution outcomes back to agent configuration to reduce recurring false positives and improve detection precision.

Data Quality & Enrichment Operations

• Maintain ops-only staging fields; manage the promote-to-production flow with audit controls.
• Design and run AI-assisted enrichment workflows (Clay + LLM prompts) with evidence links and confidence thresholds.
• Monitor fill-rate, sampled accuracy, freshness, and cost-per-record by source and segment; surface vendor performance issues and recommend changes.
• Keep data dictionaries, SOPs, and runbooks current as agents and processes evolve.

Cross-Functional Partnership

• GTM Systems (SFDC): field configuration, permission sets, automation, flows.
• Data Engineering: source availability, ID mapping, lineage (no pipeline coding).
• Reporting: define metrics and acceptance criteria; partner on dashboard requirements.

What to Expect

This is a triage environment, not a steady-state one. The function is young, the data has known gaps, and the work is to stabilize and extend, not maintain and optimize. You’ll be building the plane while flying it, alongside a small team that operates with high autonomy and a bias toward measurable outcomes. If ambiguity and mess energize you, this is the right fit.

Success Metrics (6–12 Months)

Pipeline Reliability

• Scheduled pipeline runs execute without function-lead intervention; failure-to-resolution cycle time under 24 hours for non-blocking issues.
• Agent coverage extended to new data elements as prioritized (measured by number of signals under active detection).

Detection & Resolution Quality

• Sentinel detection precision and recall improve quarter over quarter, tracked by segment.
• Concentrix resolution queue throughput and accuracy meet defined acceptance thresholds.
• False-positive rate decreases through feedback-loop refinement.

Data Quality Outcomes

• Tier-1 field fill-rates: Country ≥95%; Vertical ≥90% at ≥85% sampled accuracy; Revenue bands ≥90%.
• Hierarchy coverage 65–80%+ across target segments.
• Enterprise cost-per-record reduction of 30–40% via AI-first + selective vendor usage.

Qualifications

Required

• 3–6 years in Data Ops, Sales Ops, or GTM Ops with hands-on data quality ownership for account and contact data.
• Proficiency with Snowflake (SQL for querying, analysis, validation) and SFDC (object model, field configuration, data flows).
• Working experience with Claude Code or comparable LLM-based tooling in an operational (not just experimental) context.
• Experience designing and running AI-assisted enrichment workflows (e.g., Clay + LLM prompts) and evaluating accuracy/coverage.
• Comfort operating in a command-line environment: tmux, shell scripts, log analysis, batch process monitoring.
• Process design mindset with a bias toward measurable outcomes; strong written communication.

Strong Plus

• Experience with account/contact data vendors (D&B, ZoomInfo, Clearbit, StoreLeads) and waterfall enrichment logic.
• Python for QA scripting, sampling, or light automation.
• Familiarity with prompt engineering, confidence scoring, and AI guardrails (evidence capture, versioned prompts, QA sampling gates).

Tool Stack

• Core: Snowflake (SQL), SFDC, Claude Code, Clay
• Pipeline: Shell orchestration, Cartographer / Sentinel / Resolver agents
• Enrichment: D&B, ZoomInfo, Clearbit, StoreLeads, LLM prompts
• Nice to Have: Python, SOQL, prompt engineering frameworks
• AI Guardrails (Expected Practice): Confidence floors, evidence capture, versioned prompts, 10% QA sampling gates, audit-on-promote, drift alerts, and privacy/compliance checks. This role is expected to uphold and improve these practices, not just follow them.

Why This Role, Why Now

GTM Data Strategy & Operations stood up from scratch with no predecessor. Today the function runs on three offshore contractors and zero FTEs, managed by a single leader who is simultaneously building the agentic infrastructure, operating it in production, and driving major initiatives (hierarchy redesign, data quality assessment, vendor optimization).

The operating model is deliberately agentic AI–first: a multi-agent pipeline (Cartographer, Sentinel, Resolver, Reporting) handles detection, enrichment, hierarchy mapping, and conflict resolution at scale. This is not a future-state vision, these agents are live and processing enterprise account families in production today.

The problem: one person cannot build, operate, and extend this system while also managing strategic workstreams. The function currently covers only core Tier‑1 fields. Dozens of account, contact, and lead signals remain unaddressed. Every pipeline run, every failure diagnosis, and every offshore handoff flows through a single point of failure.

This role is the first onshore execution hire for an agent operator who can keep the system running, improve it, and extend detection and resolution coverage as GTM leadership prioritizes new data elements.

Role Summary

Sit between AI systems and GTM data. Operate, tune, and extend our agentic data quality pipeline (detection, enrichment, hierarchy mapping, conflict resolution) so it runs reliably, improves continuously, and expands to cover more of the data landscape. Own the handoff between automated output and human review, managing quality and throughput with our offshore team. You don’t build agents from scratch, but you run them, evaluate their output with GTM data judgment, and make them better.

Core Responsibilities

Agent Pipeline Operations

• Run and monitor production pipeline sessions (Cartographer, Sentinel, Resolver) across scheduled cadences; diagnose and resolve failures (API errors, session timeouts, data anomalies) without escalating to the function lead.
• Execute pipeline runs in Claude Claude and tmux; manage long-running batch processes; interpret logs and output to confirm data integrity before downstream handoff.
• Maintain pipeline orchestration scripts and configuration; extend agent coverage as new data elements are prioritized by GTM leadership.

Agent Tuning & Improvement

• Refine detection rules, prompt logic, and confidence thresholds based on output analysis and false-positive/negative patterns.
• Evaluate agent accuracy by segment (Enterprise vs. MM/SMB) and recommend rule or workflow changes backed by evidence.
• Run bake-offs (vendor vs. AI enrichment) to optimize cost, coverage, and accuracy; document results for decision-making.

Sentinel → Offshore Resolution Loop

• Own the handoff between Sentinel detection output and Concentrix triage queues; define queue structure, priority tiers, and resolution instructions.
• Monitor offshore resolution quality and throughput; refine detection rules based on patterns surfaced through triage.
• Close the feedback loop: track resolution outcomes back to agent configuration to reduce recurring false positives and improve detection precision.

Data Quality & Enrichment Operations

• Maintain ops-only staging fields; manage the promote-to-production flow with audit controls.
• Design and run AI-assisted enrichment workflows (Clay + LLM prompts) with evidence links and confidence thresholds.
• Monitor fill-rate, sampled accuracy, freshness, and cost-per-record by source and segment; surface vendor performance issues and recommend changes.
• Keep data dictionaries, SOPs, and runbooks current as agents and processes evolve.

Cross-Functional Partnership

• GTM Systems (SFDC): field configuration, permission sets, automation, flows.
• Data Engineering: source availability, ID mapping, lineage (no pipeline coding).
• Reporting: define metrics and acceptance criteria; partner on dashboard requirements.

What to Expect

This is a triage environment, not a steady-state one. The function is young, the data has known gaps, and the work is to stabilize and extend, not maintain and optimize. You’ll be building the plane while flying it, alongside a small team that operates with high autonomy and a bias toward measurable outcomes. If ambiguity and mess energize you, this is the right fit.

Success Metrics (6–12 Months)

Pipeline Reliability

• Scheduled pipeline runs execute without function-lead intervention; failure-to-resolution cycle time under 24 hours for non-blocking issues.
• Agent coverage extended to new data elements as prioritized (measured by number of signals under active detection).

Detection & Resolution Quality

• Sentinel detection precision and recall improve quarter over quarter, tracked by segment.
• Concentrix resolution queue throughput and accuracy meet defined acceptance thresholds.
• False-positive rate decreases through feedback-loop refinement.

Data Quality Outcomes

• Tier-1 field fill-rates: Country ≥95%; Vertical ≥90% at ≥85% sampled accuracy; Revenue bands ≥90%.
• Hierarchy coverage 65–80%+ across target segments.
• Enterprise cost-per-record reduction of 30–40% via AI-first + selective vendor usage.

Qualifications

Required

• 3–6 years in Data Ops, Sales Ops, or GTM Ops with hands-on data quality ownership for account and contact data.
• Proficiency with Snowflake (SQL for querying, analysis, validation) and SFDC (object model, field configuration, data flows).
• Working experience with Claude Code or comparable LLM-based tooling in an operational (not just experimental) context.
• Experience designing and running AI-assisted enrichment workflows (e.g., Clay + LLM prompts) and evaluating accuracy/coverage.
• Comfort operating in a command-line environment: tmux, shell scripts, log analysis, batch process monitoring.
• Process design mindset with a bias toward measurable outcomes; strong written communication.

Strong Plus

• Experience with account/contact data vendors (D&B, ZoomInfo, Clearbit, StoreLeads) and waterfall enrichment logic.
• Python for QA scripting, sampling, or light automation.
• Familiarity with prompt engineering, confidence scoring, and AI guardrails (evidence capture, versioned prompts, QA sampling gates).

Tool Stack

• Core: Snowflake (SQL), SFDC, Claude Code, Clay
• Pipeline: Shell orchestration, Cartographer / Sentinel / Resolver agents
• Enrichment: D&B, ZoomInfo, Clearbit, StoreLeads, LLM prompts
• Nice to Have: Python, SOQL, prompt engineering frameworks
• AI Guardrails (Expected Practice): Confidence floors, evidence capture, versioned prompts, 10% QA sampling gates, audit-on-promote, drift alerts, and privacy/compliance checks. This role is expected to uphold and improve these practices, not just follow them.

About the Role

We're looking for a Regional Sales Leader to help grow our Enterprise sales team. Reporting to the VP of Enterprise East, you'll be responsible for working with your team of Account Executives, providing feedback, coaching and training to ensure quota attainment and overall account development.  If you’re a builder who thrives in category creation, is passionate about developing empathetic, solutions-oriented and highly impact-driven teams, please apply!

If you join us, we know you’ll make a big impact!  Here’s what we’re thinking:

About You

We are seeking a results-oriented, motivated, and thoughtful sales leader who is laser-focused on recruiting and developing a team that is responsible for capturing net-new business within your assigned territory. On a day-to-day basis, you will be responsible for overseeing our Account Executives and helping them empower our customers to achieve business outcomes.

Qualifications:

• You have 5+ years of software sales leadership experience with at least 2+ years of that leading an Enterprise Sales Segment. 
• You have expert knowledge and experience in building and motivating sales teams, growing pipeline, and hitting revenue targets.
• Process oriented and data driven approach to managing forecasts in tools like Salesforce while scaling repeatable sales processes like MEDDPICC and Command of the Message
• You have consistent achievement of sales goals with high seller participation.
• You are a talent magnet! In this role, you will be asked to be on the constant lookout for the best talent to bring onboard to help us continue to build one of the best companies in the world.
• You have the curiosity to learn technical concepts and articulate them in a way that highlights business value to senior or executive stakeholders.
• Track record of developing deep relationships with key partners that align with our strategy to grow
• High sense of urgency and ownership along with empathy and compassion in your leadership style

Responsibilities:

• Consistently help your team meet or exceed quota
• Be considered a top-performing team leader by consistently exceeding team goals
• Cultivate a team of Account Executives that see consistent month-over-month success
• Serve as a mentor, leader, or coach to other new peers of the team
• Provide feedback to the executive team on a regular basis and help them steer the company in the right direction.
• Serve as an active member of the sales leadership team and build strong, collaborative relationships through the Sales, Marketing, Partnerships, Customer Success, Revenue Operations, and Legal teams
•  Work closely with cross-functional teams like SDR and Marketing to drive an account-based marketing strategy

Position Summary

We are seeking a skilled Machine Learning Engineer with approximately three years of hands-on experience designing, deploying, and maintaining production-grade machine learning systems. In this role, you will collaborate closely with data scientists, software engineers, and product teams to translate research models into reliable, scalable, and high-impact applications. You will be deeply involved in the end-to-end ML lifecycle—from data ingestion and feature engineering to deployment, monitoring, and continuous improvement—playing a critical part in shaping our machine learning platform and capabilities.

Primary Responsibilities

• Develop, deploy, and optimize machine learning models for real-world business use cases and client-facing applications.
• Partner with data scientists to operationalize predictive models and ensure scalable, maintainable, and performant production deployments.
• Design and implement data pipelines and workflows that support training, inference, and model lifecycle management.
• Work with large, complex datasets to ensure data quality, reproducibility, and reliable version control across ML workflows.
• Implement model monitoring, logging, and alerting strategies to track performance, detect drift, and support retraining cycles.
• Leverage cloud platforms (AWS, Azure, GCP) to build scalable ML solutions using managed services and infrastructure-as-code practices.
• Write clean, modular, and well-documented code aligned with MLOps and software engineering best practices.
• Stay current on emerging ML tooling, frameworks, and industry best practices to continuously enhance our platform and capabilities.

Qualifications

• Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
• 6+ years of experience in machine learning engineering, applied ML, or related software engineering roles.
• Strong proficiency in Python and experience with modern ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience with distributed data processing and compute frameworks (e.g., Pandas, Spark, Dask).
• Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Familiarity with CI/CD pipelines, testing automation, and version control using Git.
• Experience working with cloud-based ML platforms or services (e.g., SageMaker, Vertex AI, Databricks, or Snowflake ML) is preferred.
• Strong understanding of model evaluation, feature engineering, and performance optimization in production contexts.
• Excellent analytical, communication, and collaboration skills, with the ability to work effectively in cross-functional teams.

This is an exempt position. The annualized base pay range for this role is expected to be between $140,000-$180,000.  Actual base pay could vary based on factors including but not limited to experience, subject matter expertise, geographic location where work will be performed, and the applicant's skill set.  The base pay is just one component of the total compensation package for employees.  Other rewards may include an annual cash bonus and a comprehensive benefits package, including but not limited to medical, dental, vision, life and 401(k). Please note that the job title is subject to change based on the selected candidate’s experience and education.

POSITION SUMMARY:

This individual will play a critical cross-functional role supporting the Private Debt business, leading financial analysis and planning efforts, while working closely with the Tax team to incorporate tax considerations into reporting and ensure the business has clear, actionable tax visibility.

The ideal candidate is a self-starter with expertise in both financial planning and tax, capable of managing complex data, delivering actionable insights, and driving process improvement. This position will work closely with senior leadership, including the CFO, EVP & VP of FP&A and act as a key liaison with internal stakeholders and external advisors.

RESPONSIBILITIES

Financial Planning & Analysis (Approx. 60%):

• Support the delivery and ongoing enhancement of monthly reporting and analysis
• Analyze financial performance, including budget vs. actuals and key business drivers
• Assist with quarterly close processes, including accruals and allocations
• Develop and maintain financial models to support budgeting, forecasting, and long-term planning
• Contribute to improving operating P&L processes and reporting efficiency
• Support the implementation and optimization of financial systems and tools

Tax Forecasting & Strategy (Approx. 40%):

• Maintain and enhance quarterly tax forecasting and estimation processes
• Develop reporting to track partner allocations over time and ensure alignment between taxable income and cash distributions
• Support process improvements through automation and best practices to enhance accuracy and efficiency

COMPETENCIES:

• Strong analytical and modeling capabilities with the ability to synthesize complex financial data into actionable insights.
• Excellent communication skills and comfort working with executives and third-party service providers.
• High attention to detail with a demonstrated ability to manage multiple projects simultaneously.
• Strong leadership presence, with the ability to work independently and within cross-functional teams.
• Proactive and adaptable in a fast-paced, evolving environment.

REQUIREMENTS/QUALIFICATIONS:

• Bachelor’s degree in accounting, Finance, Economics, or a related field; CPA, CFA, or advanced degree preferred.
• 3–6 years of experience in tax and/or FP&A, preferably in the private equity or broader financial services industry.
• Proven experience with corporate and partnership tax concepts and IRS compliance.
• Advanced Microsoft Excel skills required, and financial modeling experience strongly preferred.
• Familiarity/Nice to have with financial and reporting systems such as NetSuite, Investran, Anaplan, Concur, SQL, and Snowflake.

This role will interview candidates for both our Boston and New York offices. 

For Massachusetts: The base salary range for this position is $110,000-$150,000. The base salary range represents the estimated low and high end for this position at the time of this posting. Consistent with applicable law, compensation may vary and will be determined based on but not limited to, the skills, qualifications, and experience of the applicant along with the requirements of the position, and Audax reserves the right to modify this pay range at any time. An employee may also be eligible for annual discretionary incentive compensation based on performance. 

Audax offers a wide range of employee benefits, including health insurance, life insurance, disability insurance, paid time off (including sick leave, parental leave, volunteer leave, and vacation), charitable donation match, family support services (including Bright Horizons and Benefit Advocate Center), and a 401(k) in addition to other benefits.

POSITION SUMMARY:

This individual will play a critical cross-functional role supporting the Private Debt business, leading financial analysis and planning efforts, while working closely with the Tax team to incorporate tax considerations into reporting and ensure the business has clear, actionable tax visibility.

The ideal candidate is a self-starter with expertise in both financial planning and tax, capable of managing complex data, delivering actionable insights, and driving process improvement. This position will work closely with senior leadership, including the CFO, EVP & VP of FP&A and act as a key liaison with internal stakeholders and external advisors.

RESPONSIBILITIES

Financial Planning & Analysis (Approx. 60%):

• Support the delivery and ongoing enhancement of monthly reporting and analysis
• Analyze financial performance, including budget vs. actuals and key business drivers
• Assist with quarterly close processes, including accruals and allocations
• Develop and maintain financial models to support budgeting, forecasting, and long-term planning
• Contribute to improving operating P&L processes and reporting efficiency
• Support the implementation and optimization of financial systems and tools

Tax Forecasting & Strategy (Approx. 40%):

• Maintain and enhance quarterly tax forecasting and estimation processes
• Develop reporting to track partner allocations over time and ensure alignment between taxable income and cash distributions
• Support process improvements through automation and best practices to enhance accuracy and efficiency

COMPETENCIES:

• Strong analytical and modeling capabilities with the ability to synthesize complex financial data into actionable insights.
• Excellent communication skills and comfort working with executives and third-party service providers.
• High attention to detail with a demonstrated ability to manage multiple projects simultaneously.
• Strong leadership presence, with the ability to work independently and within cross-functional teams.
• Proactive and adaptable in a fast-paced, evolving environment.

REQUIREMENTS/QUALIFICATIONS:

• Bachelor’s degree in accounting, Finance, Economics, or a related field; CPA, CFA, or advanced degree preferred.
• 3–6 years of experience in tax and/or FP&A, preferably in the private equity or broader financial services industry.
• Proven experience with corporate and partnership tax concepts and IRS compliance.
• Advanced Microsoft Excel skills required, and financial modeling experience strongly preferred.
• Familiarity/Nice to have with financial and reporting systems such as NetSuite, Investran, Anaplan, Concur, SQL, and Snowflake.

This role will interview candidates for both our Boston and New York offices. 

For New York City only: The base salary range for this position is $110,000- $150,000. The base salary range represents the estimated low and high end for this position at the time of this posting. Consistent with applicable law, compensation may vary and will be determined based on but not limited to, the skills, qualifications, and experience of the applicant along with the requirements of the position, and Audax reserves the right to modify this pay range at any time. An employee may also be eligible for annual discretionary incentive compensation based on performance.

Audax offers a wide range of employee benefits, including health insurance, life insurance, disability insurance, paid time off (including sick leave, parental leave, volunteer leave, and vacation), charitable donation match, family support services (including Bright Horizons and Benefit Advocate Center), and a 401(k) in addition to other benefits.

About the Position

We're looking for a Data Engineer to join the Scientific Data Intelligence (SDI) team at Formation Bio to help build and scale the data infrastructure that powers our drug development platform. In this role, your primary focus will be supporting the Data Science team — building the coherent, well-structured data models, feature engineering pipelines, and evidence layers that underpin their scientific work. You'll also be responsible for ingesting data from a wide variety of sources—APIs, files, databases, and licensed third-party datasets—and transforming it into clean, analytics-ready assets using modern data tooling.

This is a foundational engineering role for someone who takes pride in building things right: reliable pipelines, well-structured data models, and systems that others can trust and build on. You'll work closely with Data Scientists and other engineers across the SDI team to ensure that high-quality data is consistently available where and when it's needed — and structured in a way that directly accelerates scientific discovery.

The ideal candidate is deeply fluent in Snowflake and dbt, has strong opinions about data modeling best practices, and has experience handling large and complex datasets across diverse ingestion patterns. You thrive in environments where data quality and engineering rigor are treated as first-class concerns.

Responsibilities

• Partner closely with Data Scientists and other teams to build coherent data models, feature engineering pipelines, and structured evidence layers that directly support scientific analysis and machine learning workflows.
• Design and build scalable ingestion pipelines to onboard data from diverse sources including REST APIs, flat files, relational databases, and licensed third-party datasets.
• Develop and maintain robust data models in dbt, adhering to best practices around modularity, testing, documentation, and layered architecture (staging, intermediate, mart).
• Orchestrate pipelines using Dagster to ensure reliable, observable, and maintainable workflows.
• Implement data quality checks, validation frameworks, and monitoring to ensure trustworthiness of datasets across the platform.
• Collaborate with Data Scientists and analysts to understand data needs and translate them into well-structured, reusable models.
• Handle large-scale data movement and transfer scenarios, applying appropriate tooling and patterns to ensure efficiency and reliability at scale.
• Document data models, pipeline logic, and transformation assumptions to support discoverability and data governance.

About You

• You have 5+ years of experience in data engineering, with a strong track record of building and maintaining production-grade pipelines.
• You are deeply fluent in dbt and follow best practices rigorously—including modular model design, sources and refs, testing (schema + data), documentation, and environment management.
• You have hands-on expertise with Snowflake, including schema design, performance tuning, and data governance patterns.
• You have experience with at least one modern orchestration tool—we use Dagster, but experience with Airflow or Prefect is equally welcome.
• You have broad ingestion experience across source types: REST APIs, flat files (CSV, JSON, Parquet), relational databases, and vendor-licensed datasets.
• You have worked with large datasets (TB -> PB scale) and understand the engineering considerations that come with scale—partitioning, incremental loading, efficient data movement, and storage optimization.
• You're a strong data modeler who thinks carefully about how data is structured, named, and layered for downstream usability.
• You value documentation, testability, and building things others can maintain and extend.
• You can balance upfront design with speed to execution, slowing down when it counts without getting stuck in the details.
• Bonus: You have experience with data quality and observability tooling such as Elementary, Great Expectations, or similar frameworks — and you treat data quality as a first-class engineering concern, not an afterthought.
• Bonus: You have experience with Spark, Databricks for large-scale data processing workloads.
• Bonus: You have experience with large-scale data transfer tooling such as AWS DataSync, AWS S3 Transfer Acceleration, or equivalent cloud-native data movement services.
• Bonus: You have experience in healthcare or life sciences data environments, including familiarity with EHR, claims, or other biomedical datasets.

Total Compensation Range: $177,500 - $232,000

About the Position

We're looking for a Senior Data Engineer to join the Scientific Data Intelligence (SDI) team at Formation Bio to help transform Real World Data (RWD)—spanning electronic health records, claims, and other longitudinal patient data sources—into structured, analytics-ready assets. In this role, you'll be partnering closely with our Data Science team not only to model and transform data, but also to actively analyze it: answering research questions, generating evidence, and supporting scientific decision-making across our drug portfolio.

This position sits at the intersection of healthcare data engineering, real-world evidence analysis, and generative AI. While a strong foundation in building reliable, scalable pipelines is essential, you'll be equally expected to roll up your sleeves and work directly with the data—constructing cohorts, running analyses, and translating findings into actionable insights for scientific and business stakeholders.

The ideal candidate is a hybrid of data engineer and applied scientist: someone who can build the infrastructure and then use it, with familiarity in RWD study design, GenAI fluency (e.g., LLM-based entity extraction, summarization, classification), and strong technical expertise with modern data tooling. You'll play a key role in shaping how real-world patient data becomes discoverable, structured, and impactful across the organization.

Responsibilities

• Model and transform raw EHR and claims data into clean, canonical, and analytics-ready datasets using SQL, Python, and clinical standards like OMOP.
• Build and manage scalable data pipelines using Dagster for orchestration, dbt for transformation, and Snowflake as the primary compute and storage engine.
• Conduct hands-on RWD analyses to answer scientific and strategic research questions—including disease epidemiology, treatment patterns, patient journey characterization, and comparative effectiveness.
• Partner with Data Scientists and clinical leads to design and execute observational studies, translating scientific questions into well-structured, reproducible analyses.
• Implement data validation, completeness, and observability frameworks to ensure real-world datasets are accurate, comprehensive, and trustworthy for downstream research and product use.
• Apply Generative AI techniques within transformation and analysis layers to accelerate data structuring and insight generation.
• Communicate findings clearly to both technical and non-technical stakeholders, including summaries for portfolio teams and leadership.

About You

• You have 5+ years of experience in data engineering, ideally with at least 2 years working in healthcare or life sciences, including direct exposure to EHR or claims datasets.
• You have experience with ontologies and biomedical schemas (e.g. UMLS, LOINC, ICD9/10, MeSH) and understand the modalities found within RWD — billing claims, lab results, visit notes.
• You're fluent in SQL and Python, and you've built and maintained production-grade pipelines that support analytics or scientific workflows.
• You have experience building longitudinal patient cohorts from EHR or claims data, including index date logic, washout periods, and follow-up window construction.
• You have a solid understanding of the causal inference frameworks such as potential outcomes and target trial emulation. 
• You have working familiarity with real-world evidence study design concepts—such as active comparator new user designs, time-to-event outcomes, confounder adjustment, and causal discovery algorithms—sufficient to partner effectively with Data Scientists on causal inference workflows.
• You value clarity, documentation, and structured thinking—especially when working with complex healthcare data.
• You have hands-on expertise with modern data infrastructure, such as Snowflake, dbt, and Dagster.
• You can balance upfront design with speed to execution, slowing down when it counts without getting stuck in the details.
• Bonus: You've worked in regulated or privacy-sensitive data environments and are familiar with governance models for PHI or sensitive data.
• Bonus: You have prior experience working with commercial RWD vendors (e.g. Truveta, Optum, Komodo, IQVIA) and understand the nuances of licensed claims and EHR datasets, including longitudinal patient journey construction and line-of-therapy sequencing.

Total Compensation Range: $204,500 - $267,000

Data is at the heart of every decision at Klaviyo. Our Analytics Engineering team sits within a hub-and-spoke model, partnering closely with Go-To-Market (GTM), Product, Engineering, and Business Intelligence teams to build scalable, trusted data systems. You’ll work at the intersection of business and engineering, owning core data models, enabling self-service analytics, and ensuring our data ecosystem is reliable, scalable, and compliant.

About the Role

We’re looking for a Senior Analytics Engineer to own end-to-end data pipelines and modeling across GTM (Sales) and Product domains. You’ll build and maintain the source of truth for operational and product data, enabling leadership, analysts, and product teams to answer complex questions with confidence. This role combines deep technical execution with high-impact business partnership, requiring you to translate ambiguous needs into scalable data products. You will operate as a trusted, embedded partner to stakeholders while leveraging the broader Data Engineering organization’s standards, tooling, and best practices. You’ll help build the foundation that powers everything from forecasting and compensation to product insights and experimentation. Please note that this is a hybrid role that requires 3 days/week in our Boston office. Fully remote candidates will not be considered at this time. 

How You’ll Make a Difference

• Own and deliver end-to-end data pipelines and models that power Sales and Product analytics
• Build curated, governed data marts that enable fast, reliable decision-making
• Design dimensional data models (dbt) for core entities such as accounts, pipeline, performance, and product usage
• Partner with Sales, Product, and Analytics teams to create holistic views of the customer and product lifecycle
• Raise the bar on data quality, testing, monitoring, and documentation
• Enable reverse ETL workflows to operationalize insights into business systems
• Act as a data ambassador across Product, Engineering, BI, and Legal, ensuring alignment on data contracts and governance
• Lead and mentor others through design reviews, best practices, and technical guidance
• Evolve internal tooling and infrastructure to improve developer and analyst productivity

What You’ll Do

• Data Modeling & Pipelines: Build and maintain production-grade dbt models in Snowflake
• Integrations & Ingestion: Own ingestion from systems like Salesforce, ERP, and product data sources
• Reverse ETL: Push high-value data models into downstream tools and workflows
• Data Quality & Governance: Implement testing frameworks, monitoring, and documentation to ensure trust and compliance
• Cross-Functional Partnership: Work closely with Sales Ops, Product Managers, Engineering, Finance, and GTM teams
• Repository Stewardship: Maintain dbt codebase, enforce best practices, and ensure scalability
• Tooling & Infrastructure: Contribute to Airflow, Terraform, AWS, and other platform tools as needed

Who You Are

• 5+ years of experience in analytics or data engineering in a modern data stack (Snowflake, BigQuery, or Redshift)
• Strong expertise in SQL and dbt, with a focus on building scalable, production-grade data models
• Proficiency in Python (or similar) for orchestration or tooling
• Experience owning end-to-end data workflows, from ingestion to business impact
• Ability to translate ambiguous business needs into structured data solutions
• Strong communication skills with the ability to partner with non-technical stakeholders
• Experience implementing data testing, monitoring, and governance frameworks
• Proven ability to lead cross-functional projects with minimal guidance

Nice to Have

• Experience with Sales Operations, compensation, or GTM analytics
• Familiarity with reverse ETL tools and workflows
• Experience with Airflow, Fivetran, Workato, or similar tools
• AWS experience (S3, EC2, Lambda) and infrastructure-as-code (Terraform)
• Exposure to experimentation frameworks or product analytics systems
• Experience with data privacy controls (masking, role-based access, compliance)

At Klaviyo, we value the unique backgrounds, experiences and perspectives each Klaviyo (we call ourselves Klaviyos) brings to our workplace each and every day. We believe everyone deserves a fair shot at success and appreciate the experiences each person brings beyond the traditional job requirements. If you’re a close but not exact match with the description, we hope you’ll still consider applying. Want to learn more about life at Klaviyo? Visit careers.klaviyo.com to see how we empower creators to own their own destiny.

We use Covey as part of our hiring and / or promotional process. For jobs or candidates in NYC, certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 3, 2025.

Please see the independent bias audit report covering our use of Covey here

Company Overview

We are the better way to work in finance. As private equity’s value creation partner, we sit at the heart of PE—where sponsors and CFOs meet. Through financial consulting rooted in data, technology, and AI, we help clients drive value—supporting the office of the CFO to drive end-to-end value creation.

If you crave challenging work and are looking to grow, come solve complex issues alongside 1,600+ finance & technology experts in a supportive, collaborative environment.

Backed by premier private equity firms and headquartered in New York with 11 offices around the globe, we are a high-growth, entrepreneurial firm looking for people who want to be part of building something great. Come make your mark. 

Data & Analytics

Accordion's Data & Analytics (D&A) team offers cutting-edge, intelligent solutions to a global clientele, leveraging a blend of domain knowledge, sophisticated technology tools, and deep analytics capabilities to tackle complex business challenges.

We partner with Private Equity clients and their Portfolio Companies across diverse sectors, including Retail, CPG, Healthcare, Media & Entertainment, Technology, and Logistics.

We deliver data and analytical solutions designed to streamline reporting capabilities and enhance business insights across vast and complex data sets ranging from Sales, Operations, Marketing, Pricing, Customer Strategies, and more.

This role can be based in any of our US office locations and is a hybrid role with the flexibility to work remotely 2 days a week. Ideal candidates should be local to the desired location.

This position is not eligible for immigration sponsorship.

About the Role and You

Lead end-to-end delivery of modern data platforms for our clients—owning scope, schedule, budget, and outcomes. You’ll run agile delivery teams, guide medallion/lakehouse architectures, and partner with clients to translate business goals into scalable solutions on Snowflake, Azure & Databricks.

What You'll Do

• Own program delivery: Drive planning, execution, risk/issue management, RAID logs, dependencies, and executive status reporting across multi-workstream data initiatives.
• Lead agile teams: Stand up and mature Scrum/Kanban ceremonies, backlog hygiene, velocity forecasting, and release management across data engineering, analytics, and platform ops.
• Architectural stewardship: Ensure adherence to medallion (bronze/silver/gold) patterns, data contracts, CI/CD for data, and governance/quality controls (DQ rules, lineage, observability).
• Cloud & platform delivery: Orchestrate environments, security, and cost controls for Snowflake and Azure (ADF/Fabric, Databricks, Synapse, Key Vault, Purview).
• Client leadership: Serve as the day-to-day executive contact; run steering committees, manage change requests, and drive stakeholder alignment and adoption.
• Commercials & quality: Own SOW scope, staffing, and margin; forecast and track budgets; enforce delivery standards and acceptance criteria tied to business KPIs.
• Enablement: Coach PMs and tech leads; codify playbooks/templates for repeatable delivery; contribute to proposals and pre-sales as needed.
• Travel to client sites as needed

What You’ll Bring

• 5+ years of technology delivery leading data platform or analytics programs in consulting/professional services.
• Proven success managing $1M–$5M+ multi-workstream programs with distributed teams.
• Hands-on familiarity with Snowflake (warehouses, RBAC, performance/cost tuning) and Azure data services (ADF/Fabric, Databricks, Synapse, ADLS, networking & security).
• Strong command of agile delivery (Scrum/Kanban), Jira/Azure DevOps, release planning, and CI/CD for data (Git-based workflows, environments).
• Practical experience implementing medallion architectures and lakehouse patterns, data modeling, and data quality/observability.
• Stakeholder management at VP/C-suite level; crisp written/oral communication and executive reporting.
• Nice to have: regulated industries, FinOps for data platforms, data privacy/compliance (GDPR/CCPA), and analytics enablement (Power BI/Fabric).

Preferred requirements

• BS/MS in CS, IS, Engineering, or equivalent experience.
• PMP, PMI-ACP, or SAFe; Snowflake SnowPro; Microsoft Azure (DP-203, AZ-305) a plus.

What Success Looks Like

• Programs delivered on-time/on-budget with measurable business outcomes (e.g., SLA improvements, cost per query reductions, faster time-to-insight).
• Stable, secure, and cost-efficient Snowflake/Azure platforms with clear operating models and playbooks.
• High client satisfaction (CSAT/NPS) and referenceable accounts.

The annual salary for this role ranges from: $112,500 to $200,000 USD + benefits + bonus. Actual compensation packages are determined by evaluating a wide array of factors unique to each candidate, including but not limited to geographic location, skill set, years and depth of experience, education, certifications, cost of labor and internal equity.

Accordion is an Equal Opportunity Employer. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills.  We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical​​​ condition, pregnancy, genetic information, gender, sexual orientation, gender identity or ​expression, veteran status, or any other status protected under federal, state, or local law.

Please note that as per Accordion policy, we do not accept unsolicited resumes from third-party recruiters unless such recruiters are engaged to provide candidates for a specified opening and in alignment with our inclusive diversity values. Any employment agency, person or entity that submits an unsolicited resume does so with the understanding that Accordion will have the right to hire that applicant at its discretion without any fee owed to the submitting employment agency, person or entity.

#LI-BS1

Location:  Northeast, US

REQ ID - FEQ327R20

We are looking for pre-sales professionals who have a successful track record helping large enterprises become more data-driven. Working with the Enterprise Account Executive (AE), the Sr. Solutions Engineer defines and directs the technical strategy for our largest and important accounts, leading to more widespread use of our products and wider and deeper adoption of ML & AI. You will lean upon your background in value selling, technical account management and technical leadership to maximize success in these accounts. While you work with a team that includes hands-on resources who will build proofs of concept and demonstrate Databricks' products, you need to be technical and must understand the relevance and application of ML & AI within a range of use cases important to the target accounts in the greater region.

The impact you will have:

• You work with multiple clients as the main technical voice for Databricks.
• You lead your customers on a transformational journey, helping them to evaluate and adopt Databricks as part of their strategy
• You implement the technical strategy in the account, in close understanding of the strategy.
• You build a movement of technical champions within the account.
• You align technical strategies around Databricks solutions.
• You work with your sales team and technical peers to drive business outcomes for our clients

What we look for:

• Proficiency at establishing virtual teams, and leading them to ultimate success within the account.
• Experience working very large (> $1m ARR), global accounts.
• Form relationships with executives and influencers.
• Present a convincing point-of-view to important decision-makers that leads them down a path of success.
• Technical in big data, data science and cloud (Azure & AWS).
• An ability in data-driven business transformation, and driving change with data.
• Production programming experience in Python, R, Scala or Java
• Nice to have: Databricks, Snowflake, AWS or Azure Certification

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.

Overview 

Velir x Brooklyn Data is seeking a Director of Data & AI Alliances to build and scale strategic partnerships across the modern data and AI ecosystem. This role will drive joint go-to-market initiatives, co-sell motions, and revenue growth with leading data platform providers. 

As a data and AI-led digital agency, Velir x Brooklyn Data partners with organizations to unify data, activate AI, and deliver intelligent digital experiences. This role will be responsible for strengthening Velir x Brooklyn Data's position within the data ecosystem and translating partner relationships into meaningful pipeline and revenue. 

The ideal candidate is both strategic and commercially driven, with deep experience building alliances with leading data, analytics, and AI platforms.

Responsibilities

Strategic Alliance Development 

• Build and manage Velir x Brooklyn Data’s strategic partnerships with leading data and AI platform providers, including Snowflake, Databricks, Domo, and dbt. 
• Identify and develop new alliance opportunities within the modern data stack ecosystem. 
• Strengthen Velir x Brooklyn Data’s positioning as a preferred services partner within partner ecosystems. 
• Identify and pursue “first-to-market” opportunities, including early adoption of new partner features, products, or capabilities. 

Revenue and Pipeline Generation 

• Develop and execute joint go-to-market strategies that generate pipeline and new business opportunities. 
• Work closely with Velir x Brooklyn Data’s sales and growth teams to support co-selling initiatives and partner-led opportunities. 
• Proactively track and register opportunities to ensure the organization receives appropriate credit for sourced opportunities and client consumption growth. 

Partner Relationship Management 

• Serve as the primary Velir x Brooklyn Data liaison with key partner executives and alliance leaders. 
• Establish and nurture executive relationships across partner organizations. 
• Maintain strong collaboration between Velir’s sales, marketing, and delivery teams and partner counterparts. 

Partner Event & Ecosystem Strategy 

• Lead Velir x Brooklyn Data’s strategy and execution for data and AI partner conferences, summits, and ecosystem events. 
• Identify and prioritize the most impactful partner events including Snowflake Summit, Databricks Data + AI Summit, Domo Palooza, and dbt Coalesce. 
• Develop pre-event go-to-market strategies to drive pipeline generation, partner alignment, and client engagement. 
• Coordinate internal preparation across sales, marketing, and delivery teams to ensure Velir x Brooklyn Data is positioned effectively throughout event preparation, execution, and follow up 
• Oversee execution of Velir x Brooklyn Data’s presence at partner events including executive meetings, speaking opportunities, joint marketing initiatives, and partner engagement activities. 
• Ensure strong post-event follow-up and pipeline conversion from meetings, leads, and partner introductions generated at these events. 

Joint Marketing & Market Positioning 

• Identify opportunities to showcase Velir x Brooklyn Data’s expertise in data, AI, and modern digital experience transformation. 
• Drive the development of early case studies and success stories that support partner adoption and strengthen market positioning. 
• Maintain visibility into partner product and feature roadmaps to proactively identify opportunities for early testing, case studies, and joint go-to-market efforts 

Enablement & Internal Alignment 

• Ensure teams are enabled on partner technologies and value propositions. 
• Align internal stakeholders on partner priorities and initiatives. 
• Support joint solution development and partner-aligned service offerings

Qualifications

Required Experience 

• 7+ years of experience in technology alliances, partnerships, or ecosystem leadership 
• Experience working with data platforms, analytics, or AI technology ecosystems 
• Proven success driving partner-sourced or partner-influenced revenue 
• Experience building joint GTM strategies and co-sell motions 

Preferred Experience 

• Background in data platforms, analytics, AI, or digital transformation services 
• Experience working with platforms including, but not limited to: 
  • Snowflake 
  • Databricks 
  • Domo 
  • dbt 
• Experience working in consulting, agency, or professional services environments 

Skills & Attributes

• Strategic partnership development 
• Executive relationship building 
• Revenue-focused alliance management 
• Strong communication and storytelling 
• Deep understanding of the modern data stack 
• Demonstrated curiosity about emerging data technologies. 
• Ability to work cross-functionally with sales, marketing, and delivery teams 

Physical Requirements 

• Frequent sitting at a desk performing work on a computer. 
• Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. 
• Travel will be required  

Please note that compensation packages are finalized after the interview process is concluded. We use a competency-based approach to base pay, which means it is based on the competencies and skills demonstrated for this role.

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.

The Customer Success Data and Analytics (CSDA) team supports Care Operations, Customer Success, Education, and Voice of the Customer. We are building a new generation of data products that extract structured, actionable insight from unstructured customer interactions using NLP, ML, and LLM-based approaches.

As the Principal Data Product Manager, Applied AI, you will own the agenda for how AI-enriched data improves our understanding of the customer experience. You will prototype, validate, and scale a portfolio of AI-powered data products that give our CS organization insight it has never had before. You will also serve as a resource and thought partner for teammates and other teams across Toast who are working with CS data in an AI context, helping make that work faster, more consistent, and more reliable.

This is a hands-on, high-ownership role. You will spend time in Snowflake and Hex building things, and you will spend time with CS leaders explaining what those things mean and why they matter. The right person for this role is equally comfortable doing both.

A Day in the Life (Responsibilities)

Enablement and Shared Practice

• Technical Resource: Serve as the go-to resource on applied AI and NLP with CS data for your CSDA teammates and other teams across Toast.
• Best Practices: Build and maintain a practical library of guidance regarding successful prompts, pitfalls to watch for, and how to structure CS data for AI use cases.
• Market Intelligence: Stay current on how CS data is being used with AI tools across the organization to keep CSDA ahead of the curve.
• Facilitation: Act as a facilitator for AI prototyping work, helping analysts and stakeholders structure experiments so outputs are reliable and usable.

Applied AI and ML Product Development

• Portfolio Ownership: Drive an active portfolio of prototyping work focused on enriching the CSDA data model with insight extracted from unstructured contact data.
• Strategic Focus: Focus on contact classification, intelligent summarization, sentiment analysis, churn signal detection, and product mention extraction.
• End-to-End Design: Manage the pipeline from problem framing and prompt design to embedding generation, classifier training, and final production.
• Stakeholder Validation: Run structured validation processes, including codebook reviews and labeling sessions, to earn trust before products scale.

Scaling and Productionalization

• Readiness Framework: Maintain a clear framework for deciding what is ready to scale based on business value, quality, and maintenance requirements.
• Engineering Partnership: Partner closely with the CSDA data engineering team to scope and hand off production-grade implementations.
• Quality Governance: Own the quality review process for AI products in production, including monitoring for output drift and driving recalibration.
• Product Advocacy: Serve as the face of these products with stakeholders, explaining how they work and being honest about their limitations.

What You’ll Need to Thrive (Requirements)

• Relevant Experience: 6+ years of experience in analytics, data science, ML, or NLP in a data-intensive environment.
• AI Fluency: Practical fluency with LLMs, including prompt engineering, structured output extraction, and model evaluation.
• Product Instincts: You think about what you are building in terms of who uses it and how you will know if it is working.
• Project Leadership: Strong program and stakeholder management skills with the ability to move things forward without formal authority.
• Technical Expertise: Applied experience with text data and NLP/ML techniques such as LLMs, classifiers, or embeddings.
• Core Skills: Expert-level SQL and solid Python proficiency with the ability to build and iterate on ML pipelines independently.
• Communication Mastery: Ability to explain complex classification pipelines to non-technical leaders and push back on requirements when necessary.

What Will Help You Stand Out (Nice to Haves)

• Specific Tooling: Experience with Snowflake Cortex, including COMPLETE and EMBED_TEXT functions.
• Library Familiarity: Familiarity with scikit-learn, spaCy, Hugging Face, or sentence-transformers.
• Domain Expertise: Prior work in a Customer Success, Care, or CX analytics context.
• Workflow Design: Experience building human-in-the-loop validation workflows or labeling interfaces.
• Proven Impact: A track record of prototyping things that made it into production and were successfully used.

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.

Role overview

Car shopping is complicated. At CarGurus, we use data and technology to make it simple, giving people the tools they need to confidently find, buy, finance, or sell a car. The best part? Our work makes a real impact. We’re the most-visited car-shopping site in the US and we are growing fast in our international markets. Ready to come along for the ride? 

The Data Engineering team at CarGurus is looking for a Senior Manager of Analytics Engineering to enable data-driven decisions across the company. You will be responsible for delivering scalable and highly performant data models that power reporting solutions for stakeholders in Product Management, Marketing, Finance, and other key areas of the business. We are looking for a candidate who can work collaboratively with our partners and customers, who can also actively contribute to our corporate analytics strategy as we continue to grow and evolve our core business.  

What you'll do

• Lead a team of analytics engineers who will support a diverse set of stakeholders across our business and engineering orgs.
• Provide tactical leadership to your team to build production quality data transformations and maintainable data architectures.  
• Partner on a vision for CarGurus’ business intelligence platform, identifying gaps in our people and technology strategies.
• Build strong partnerships across the company and continuously integrate feedback into the team’s strategy and operations.
• Collaborate with Product Management to prioritize initiatives, manage the execution of projects, and implement agile software development practices as needed.
• Work in conjunction with the Data Governance team to ensure high data quality standards are understood and met.

What you'll bring

• You are an experienced leader with a track record of managing and developing high-performing teams. You excel at coaching, mentoring, and creating growth opportunities for your team members, helping them achieve their professional goals.
• You have a deep understanding of data analytics principles and experience overseeing complex data landscapes, guiding technical direction, and maintaining high standards for data quality and reliability.
• You possess strong communication and collaboration skills, allowing you to work effectively with cross-functional teams, including Product, Analytics, and other Engineering leaders, to drive alignment and achieve business goals.
• You’ve worked with distributed analytics teams.
• 8+ years of experience in product/business analytics, data engineering, analytics engineering, or related field
• 3+ years of hands-on people management experience
• Experience working with a cloud data warehouse (e.g., Snowflake, Databricks, Google BigQuery) and a modern business intelligence platform (e.g., Tableau, Looker, Sigma)
• Expert level SQL proficiency and deep familiarity with dimensional modeling best practices
• Ability to build a multi-sprint plan for a complex data modeling project from loosely defined business requirements 

Role overview

As a core member of the Data Science team, the Principal Data Scientist will lead the design and development of high-impact machine learning systems that power CarGurus’ core products and strategic initiatives.  You will own models and data pipelines end-to-end from problem framing and experimentation through production deployment, monitoring, and continuous improvements. Potential areas of support and ownership include Recommendations, Search Ranking, Instant Market Value algorithms, and new ML-driven product capabilities.

What you'll do

• Own the end‑to‑end design and implementation of production ML models and systems, including model architecture, feature strategy, and evaluation methodology. 
• In partnership with Data and Analytics teams, develop and maintain data pipelines to supply training and inference data for models, using SQL and Snowflake.
• Collaborate with engineering leaders on system and API design to ensure ML solutions meet requirements for latency, reliability, observability, and maintainability in production.
• Apply best practices for experimentation and model evaluation, including offline metrics, A/B testing design, and post‑launch analysis; coach other data scientists in applying these practices rigorously.
• Communicate solutions to stakeholders through written documentation, demos and presentations, and data visualizations tailored to both technical and non‑technical audiences.

What you'll bring

• 7+ years of experience in Data Science or Machine Learning roles, with a consistent track record of shipping, owning, and iteratively improving production ML systems that drive material business impact.
• Deep expertise in Machine Learning techniques for supervised and unsupervised learning across structured and unstructured datasets. Comprehensive knowledge of, and real-world experience with, measurement, evaluation, and testing of models.
• Proven experience deploying and maintaining machine learning services in production, ideally in a cloud environment (e.g. AWS, SageMaker, Snowflake).
• High proficiency in Python (or a similar languages widely used in the data science community) and SQL, plus experience establishing coding, testing, and reproducibility standards (e.g., shared libraries, experiment tracking, templates).
• Ability to communicate technical details, trade‑offs, and analytical findings to audiences ranging from engineers to senior business leaders, using clear narratives and data‑driven recommendations.
• Advanced degree (or proven experience) in Computer Science, Data Science, Mathematics, or any quantitative science which makes use of advanced data analytics or statistical or machine learning techniques

Role overview

Our Information Security team is responsible for ensuring the security of our customers and the safety of our data. As the Manager of Security GRC, you will guide the evolution of our established GRC function. You aren't just maintaining a program; you are maturing our capabilities to ensure that security is a tailwind for our business, transforming complex regulatory requirements into a competitive advantage.

You will be a strategic leader who balances high-standard execution with a focus on Revenue Enablement, ensuring our security posture removes friction from the enterprise sales cycle and reinforces our market position as a trusted partner.

What you'll do

• Take ownership of an established team to elevate our GRC maturity. You will develop and refine our Integrated Management System (IMS) across ISO 27001, 27017, 27018, and SOC 2 Type II.

• Modernize our risk reporting by leveraging quantitative risk management. You will move beyond qualitative "Red/Yellow/Green" charts to provide real-time, data-driven insights and financial risk projections using FAIR principles.

• Serve as a leading voice on our AI Governance Committee. You will guide the secure adoption of AI/LLM features within our product and oversee the governance of AI integration across our internal SaaS ecosystem, aligned with ISO 42001.

• Focus on GRC as a revenue driver. By maturing our compliance and risk functions, you will ensure our security trust posture supports global growth and instills immediate confidence in our largest enterprise customers.

• Partner with Product and Engineering to validate and mature technical controls within cloud environments (e.g. AWS, GCP) and cloud data warehousing environments (e.g. Snowflake). You will ensure that compliance is a seamless part of the CI/CD pipeline and agile software development lifecycle.

• Provide expert guidance on GDPR and CPRA, ensuring our risk management strategies remain resilient in a rapidly changing global privacy landscape.

What you'll bring

• You have 7+ years in Information Security and a track record of maturing established teams. You know how to keep team performance on track while maintaining momentum toward strategic goals.

• You have a deep understanding of AWS security services and Snowflake data governance. You are comfortable challenging and supporting technical teams to innovate and solve strategic challenges.

• You embody a growth mindset and use data and facts to inform priorities. Experience with SAFE Security or similar CRQM platforms is a significant plus.

• A Change Leader: You are comfortable with ambiguity and adapt quickly when priorities change. You encourage experimentation within your team to automate evidence collection and risk discovery.

• A Caring Collaborator: You lead with empathy and inclusion, building diverse teams that support one another. You are a cultural ambassador who can see things from others' viewpoints and reconcile multiple stakeholder views to drive results.

• A Clear Communicator: You bring people along with your vision. You can translate technical risk into business value for executive decision-makers and functional leaders.

Our Leadership Competencies:

We hire leaders who are a Catalyst for Impact, a Coach to their peers, a clear Communicator, an innovative Change Leader, and a Caring Collaborator. In this role, you will be expected to:

• Set high standards and take full ownership of the GRC roadmap.

• Proactively manage the growth of team members through clear feedback and experiential opportunities.

• Engage key decision-makers across business functions to support complex, cross-functional initiatives.

• Navigate organizational dynamics to establish accountability and ensure successful delivery of results.

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.

Are you passionate about data? How about leveraging data to drive meaningful impact across a fast growing two-sided marketplace? Have opinions on how to best enable data scientists across a billion-dollar company to do elastic workforce planning or real-time customer lifetime value prediction? Then we should definitely talk!

The Data Technology team at ezCater is growing! As we look towards the second half of 2025 and beyond, data is a key strategic component across the company - from advanced, real-time machine learning to business intelligence and data governance. Data is our differentiator and how we will drive real, meaningful impact to the $100+billion Catering industry.

We are hiring a Senior Data Engineer to join our expanding team in solving complex data and platform challenges to accelerate our growing business. The ideal candidate lives and breathes data while driving systems and architecture best practices. They care about driving business impact through producing solid and efficient infrastructure alongside accurate and performant data.  You will have the opportunity to work directly with executive stakeholders as we embark on a massive-scale data modeling effort across the organization, so flexibility and the ability to translate business requests to implementation are key.

What you’ll do:

• Write and ship a lot of code.
• Work directly with analysts and stakeholders to refine requirements, nail down logic, and debug and qualify produced data sets to ensure they meet the underlying business needs.
• Drive architectural decisions and thoughtfully balance trade-offs in system design.
• Lead cross-functional data initiatives.
• Champion high standards in data quality, security, and discoverability.
• Translate complex technical challenges into clear solutions.
• Design and develop high-performance data pipelines. Adhering to SDLC, including CI/CD and best practices.
• Identify and drive opportunities to optimize and/or scale existing parts of our stack.
• Utilize tooling and automation to improve developer efficiency.
• Monitor data systems to ensure quality and availability while seeking to drive down costs.
• Contribute to the team processes and community.
• Mentor other Data Engineers.
• Be part of our innovation and transformation story.
  
  
  

What you have:

• Strong experience with data warehousing, data lakes, ELT process, and enterprise data platforms such as Snowflake (preferred), Redshift & BigQuery.
• Experience with building performant data pipelines across disparate systems.
• Experience with cloud platforms such as AWS (preferred), GCP & Azure.
• Mastery in SQL and experience in Python.
• Ability to work independently and collaboratively.
• Willingness and ability to be part of an on-call rotation, occasionally working non-business hours to address critical system alerts and maintain service uptime.
• An open mind and willingness to be flexible. We have a large and complex business, and believe in driving real value out of every project we do.
• Our stack is Snowflake, dbt, Fivetran, Airflow, AWS, Sagemaker, MLFlow, Kubernetes + Docker, Monte Carlo, Hightouch and Python for custom ETL and data science integrations. Experience with the above is a nice-to-have, and a desire to learn is a must.
• Experience with marketing data is preferred, but not required. 
• Ability to travel up to 5 days per quarter for Together Weeks, team gatherings and other events, when applicable.
• A sharp mind, a soft heart and a large funny bone.
  

The national total target cash compensation range for this position, including base salary and bonus target, is $158,000–$190,000 annually.*

*Please note: Final offer amounts are determined by multiple factors, including prior experience, expertise and region & may vary from the amount above. This range does not represent additional compensation benefits (such as equity, 401K or medical, dental or vision insurance).

This is a critical, senior leadership role where you will strategically own and elevate ezCater's entire data-driven optimization ecosystem, encompassing site analytics, advanced behavioral insights, and experimentation across all customer and supplier experiences. This role serves as the central point of truth for user interaction understanding, from discovery and conversion to fulfillment and delivery 

The ideal candidate is obsessed with maximizing business outcomes through optimization, data science, and relentless customer-centric experimentation. You will lead, mentor, and grow a high-performing team of Analysts and Data Scientists, empowering them to generate new predictive signals, diagnose complex friction points, and shape the end-to-end user journey. You will monitor and interpret performance across the full conversion and engagement funnel, connecting data to measurable business impact 

You will partner deeply with Product, Design, Engineering, and Marketing leadership to define the data strategy and institutionalize a culture of rigorous, data-informed decision-making 

What You’ll Do

• Own end-to-end site analytics and performance trends, including funnel health, conversion metrics, and behavioral analysis across all key customer and supplier journeys 
• Design and execute advanced statistical models and analyses to deeply understand user behavior, identify key drivers of growth, and forecast business metrics 
• Oversee and interpret A/B/n test results from experimentation platforms (like Eppo), providing definitive, statistically sound guidance on design, content, and feature decisions 
• Lead executive-level metric and trend reviews, translating complex data science and analytical findings into clear, actionable business strategies for the broader Product & Technology leadership team 
• Lead and develop a team of Analysts and Data Scientists, setting the strategic roadmap for data insights, model deployment, and experimentation practices across the organization 
• Experience leading a team responsible for establishing and maintaining data governance frameworks for web analytics and behavioral data, ensuring high fidelity, integrity, and compliance across all instrumentation (FullStory, Amplitude, etc.) 
• Need understanding of technical roadmaps for data tooling, including the selection and configuration of web analytics, session replay, and experimentation systems to maximize team efficiency and insight generation 
• Experience leading teams responsible for integrating AI/ML to enhance site personalization, predictive customer segmentation, and automated anomaly detection, moving beyond descriptive reporting to prescriptive action 

What You’ll Bring

• 10+ years of progressive experience in digital product, web analytics, or data science leadership for a B2B/B2C technology, e-commerce, or marketplace company 
• Proven experience leading, mentoring, and scaling a team that includes both product analysts and data scientists 
• Expert-level familiarity with the modern data stack: SQL, Python/R, cloud data warehouses (Snowflake), and advanced product experimentation tools (Eppo, Optimizely, LaunchDarkly) 
• Exceptional critical thinking, with a deep understanding of advanced funnel analysis, causal inference, A/B testing methodologies, and behavioral diagnostics 
• Demonstrated ability to influence product and business strategy by communicating complex technical findings and data science concepts clearly to executives and cross-functional partners 
• Experience in defining site experience KPIs, event instrumentation requirements, and running high-impact Conversion Rate Optimization or web experiment roadmaps 
• A strong strategic bias toward action, using data not just to report on performance but to proactively connect insights to future business outcomes 
• Need understanding of AI/ML techniques and their application to business problems such as personalization, predictive modeling, and demand forecasting 
• Deep familiarity with core analytics platforms: Amplitude, Adobe Analytics, DataDog, and qualitative tools (FullStory, Hotjar) 
• Ability to travel up to 5 days per quarter for Together Weeks, team gatherings and other events, when applicable.

The national total target cash compensation range for this position, including base salary and bonus target, is $248,000 - $345,000 annually.*

*Please note: Final offer amounts are determined by multiple factors, including prior experience, expertise and region & may vary from the amount above. This range does not represent additional compensation benefits (such as equity, 401K or medical, dental or vision insurance).

Please have fun with the Cover Letter portion of the application! It does not need to follow “traditional” cover letter guidelines - we would love for you to write 150-500 words explaining why you are interested in ezCater and the role, and highlighting anything else you think we should know!

Overview

Velir x Brooklyn Data is looking for a Sales Hunter to drive new customer acquisition for our Data and AI consulting practice. This is an individual contributor role for a strategic seller who thrives on the "chase." You will be responsible for opening doors, working closely with our Data & AI partners, navigating a variety of situations including complex enterprise sales cycles, and positioning Velir x Brooklyn Data as the premier trusted partner for strategic consulting all the way through to modern data stack solutions. Your success will be measured by your ability to consistently build a pipeline and be that “sales QB” that drives all the way through the sales process and close high-value, data-driven solutions that solve our client’s business problems.

Responsibilities

• Outbound Prospecting: Identify and penetrate new accounts through direct hunting, social selling, and leveraging your existing network.
• Partner Ecosystem: Proactively co-sell with key technology partners (Snowflake, Databricks, dbt, Domo, Sigma and others) to identify and win "new business" opportunities.
• Full-Cycle Sales Ownership: Lead the entire sales process using value-based selling frameworks (e.g., MEDDPICC or Challenger) from initial cold outreach and qualification to final contract negotiation and closing.
• Strategic Territory Development: Build and execute a "go-to-market" plan for your assigned territory or vertical to maximize Velir’s market share in the Data & AI space.
• Expert Solution Positioning: Confidently articulate the value of modern data architectures, AI/ML strategies, and analytics to C-level executives and technical stakeholders. We sell with a consultative, solution-based approach.
• Pipeline Management: Maintain a clean and predictable sales pipeline, ensuring consistent movement of deals through the funnel to hit annual revenue targets.

Skills & Qualifications:

• 10+ years’ experience in a specialized "Hunter" sales role within data, AI, software or SaaS.
• Proven Track Record: A documented history of exceeding quotas and winning new logos in the mid-market and enterprise space.
• Partner Network: Established, "ready-to-call" relationships with channel managers and account executives at Snowflake, Databricks, Dbt, Domo, or Sigma is an absolute plus.
• Modern Data Stack Literacy: High "data IQ"—you don't need to write SQL, but you must be able to discuss data modeling, cloud warehouses, and AI use cases fluently.
• Executive Presence: The ability to navigate and influence "the room" all the way up to the VP and C-Suite level.
• Community Engagement: Active participation in the modern data and AI community (e.g., LinkedIn, dbt meetups, or local data events) to build brand authority and influence.
• Self-Starter Mentality: You don't wait for inbound leads; you love being a hunter where you solve customer problems, create solid relationships and want to get rewarded for it.
• Ability to travel up to 25%

Physical Requirements

• Frequent sitting at a desk performing work on a computer
• Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions
• Travel will be required for this role

Compensation Range: Base compensation annually plus competitive commission plan

Please note that compensation packages are finalized after the interview process is concluded. We use a competency-based approach to base pay, which means it is based on the competencies and skills demonstrated for this role.

The Senior Marketing Integrations Technology Specialist sits within the Marketing Operations team and manages the architecture, connectivity, and reliability of the martech stack – ensuring clean, secure data flows between marketing platforms (HubSpot, Iterable, CDP, web and ad tools), core product databases, and our data warehouse (Snowflake).

In this role, you’ll help build and maintain integrations, monitor uptime and data quality, and document how systems connect, partnering closely with Data, Platform, Engineering, Product, and Analytics teams. You’ll help turn our GTM systems into a well‑instrumented platform that Marketing can trust and scale on.

What You'll Do:

• Design and govern martech architecture, integrations, and data flows.
• Configure, maintain, and assist in implementing integrations across HubSpot, Iterable, Salesforce, CDP (ActionIQ), enterprise data hub (Snowflake), web tools, and ad/digital platforms, using native connectors, APIs, and tools like webhooks, reverse‑ETL, and low‑code automation (e.g., Zapier, Hightouch).
• Design and support integrations for SMS and mobile/app data, ensuring device IDs, push tokens, in‑product events, and messaging events flow into the right platforms for push and product messaging, as well as sending and receiving marketing SMS in a compliant way.
• Partner with cross-functional teams to wire new marketing data from core data tables to our marketing platforms so campaigns and experiments have the data they need.
• Develop a deep understanding of how marketing teams use data across channels in an effort to help design the right data schemas, attributes, and transformations needed to activate complex cross‑channel campaigns. 
• Govern martech data mappings and shared definitions for how marketing data moves across the tech stack, maintaining clear systems of record and consistent field/value definitions for attributes shared across platforms.
• Support light web development and experimentation work in partnership with Web and Digital Operations, including updating WordPress templates/pages, configuring and integrating web forms, and implementing A/B tests or experiments — with a focus on instrumenting events and connecting web data into analytics and marketing platforms to unlock richer data flows and reporting.
• Monitor reliability, uptime, and data quality for marketing systems.
• Set up and maintain monitoring, alerts, and runbooks for key integrations and data flows (e.g., sync failures, unusual drops in volume, schema changes), partnering with Data teams on logs and status pages where available.
• Proactively identify and resolve integration issues impacting campaign execution, audience availability, or reporting; drive root‑cause analysis and document fixes.
• Enable Marketing and cross‑functional partners.
• Translate marketing and GTM use cases into clear technical requirements for integrations and data flows.
• Collaborate with Campaign Operations, Digital Operations, and Data Operations to ensure they have the events, fields, and audiences needed for lifecycle journeys, promos/loyalty, ABM, and web experiments.
• Create lightweight documentation (diagrams, runbooks, “how it works” pages) so other MOTA teammates and stakeholders can understand system behavior without guesswork.
• Assist with day‑to‑day technical administration and for key marketing platforms within the Data Integrations & Technology Operations remit (e.g., managing API credentials, connection health, and integration configuration).

What You Have: 

• 5+ years of experience in marketing operations, martech, data integration, or similar technical GTM systems roles, ideally in a B2C, B2B, or B2C2B environment.
• Hands‑on experience integrating and administering marketing and GTM platforms such as marketing automation/journey tools (Iterable or similar), CRM (HubSpot and/or Salesforce), or ad/digital tools.
• Comfort working with APIs, webhooks, and integration tooling (e.g., reverse‑ETL, low‑code automation) to connect systems that don’t integrate natively.
• A systems‑thinker mindset: you enjoy mapping data flows, understanding schemas, and defining standards that make complex stacks more coherent for everyone.
• Strong collaboration skills and the ability to partner with Engineering, Data Tech, Product, Analytics, and non‑technical marketers, translating between business outcomes and technical requirements.
• Experience with web and experimentation tools such as WordPress (or similar CMS), web form builders, and A/B testing or experimentation platforms, and an understanding of how to integrate them into the broader martech and analytics stack.
• High attention to detail, a love of process and documentation, and comfort owning technical work.
• Experience with warehouse‑centric architectures.
• Familiarity with CDP, ABM, or app/event tracking implementations and the data they expose for marketing.
• Basic SQL, Python, or similar to inspect and troubleshoot data issues.
• Ability to travel up to 5 days per quarter for Together Weeks, team gatherings and other events, when applicable.

The national total target cash compensation range for this position, including base salary and bonus target, is $106,000–$138,000 annually.*

*Please note: Final offer amounts are determined by multiple factors, including prior experience, expertise and region & may vary from the amount above. This range does not represent additional compensation benefits (such as equity, 401K or medical, dental or vision insurance).

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.

Join us as a Senior Analyst in our thriving Marketing Analytics team, where you'll unleash the power of data to fuel innovation. Collaboration is at the heart of what we do, bridging Marketing, Analytics, Data Engineering, Customer Service, and Product teams. You'll be the trusted partner of a skilled peer group, playing a pivotal role in surmounting complex analytical challenges that fortify our go-to-market strategies. This role isn't just about data; it's about driving innovation and making a tangible impact. 

Here, you'll shape our customer strategy using cutting-edge marketing tech. Data is our compass, guiding every decision, especially within our marketing endeavors. If you're passionate about pioneering retention strategies, amplifying performance measurement, establishing impactful reporting, conducting A/B tests, and quantifying the influence of our marketing initiatives on customer order activity, we're excited to connect with you. Your journey to shaping the future starts here!

What You'll Do: 

• Collaborate closely with senior leaders on exciting business problems, actively shaping the team's strategic direction and focus
• Elevate customer personas and segmentation for personalized marketing, deepening our understanding of target audiences
• Analyze retention and ezRewards Loyalty Program performance, identifying growth opportunities and implementing action plans with cross-functional teams
• Develop insights through in-depth analysis, models, and reports, uncovering new marketing growth opportunities and informing business performance strategies
• Lead the evolution of our Marketing team's experimentation process and documentation, fostering a structured framework for innovation
• Enhance data visualization tools for informed decision-making and empower Marketing teammates
• Utilize data-driven analyses, A/B tests, and statistical models to uncover customer behavior insights and optimize marketing strategies
• Share marketing domain expertise, acting as a thought leader and mentor to peers for collaborative growth

What You Have: 

• 3+ years of relevant quantitative or technical analysis experience, preferably in marketing or e-commerce
• Strong SQL skills for data manipulation and analysis, coupled with experience in statistical modeling (R or Python preferred)
• Proficiency in marketing attribution methods, return on ad spend, and experimental test design (A/B testing)
• Familiarity with diverse marketing tactics including optimizing customer retention through strategies such as Email, SMS, Push, SEO/SEM, Direct Mail, and more
• Knowledge of modern data exploration and visualization tools like Snowflake, Tableau, Looker, Google Data Studio, or PowerBI, and proficiency in Google Analytics or similar digital analytics platforms
• Effective communication skills to convey data insights across different audiences
• Independent worker with cross-functional collaboration ability and adeptness in presenting insights to stakeholders
• Strong organizational skills, time management, and multitasking capability
• Ability to travel up to 5 days per quarter for Together Weeks, team gatherings and other events, when applicable.

The national total target cash compensation range for this position, including base salary and bonus target, is $119,670-$145,743 annually.*

*Please note: Final offer amounts are determined by multiple factors, including prior experience, expertise and region & may vary from the amount above. This range does not represent additional compensation benefits (such as equity, 401K or medical, dental or vision insurance).

Location: US or Canada
Type: US Applicants - Full-Time; Canadian Applicants - Independent Contractor

About Human Agency

We’re scaling rapidly and have a growing pipeline of opportunities that demand exceptional talent across disciplines. Our mission is to bring on individuals, from creative producers to technical experts to entrepreneurial leaders, who can help us realize this next chapter of growth.

We are a company of doers. Leaders roll up their sleeves, teams work flat, and everyone contributes to what ships. Titles don’t insulate us from feedback or basics. We invite critique, learn quickly, and keep raising the bar. The best ideas win here, no matter where they come from, because clients trust us to deliver the strongest outcomes every time.

Our clients’ missions, products, and bottom lines are sacred. We immerse ourselves in their world, becoming stewards of their goals and partners in solving big problems. Every product, strategy, or asset we create must be both beautiful and functional; practical, usable, and designed for real-world impact.

Humans are our most valuable resource, and we only grow by hiring people who push us forward. Across strategy, engineering, design, data, and operations, we seek out teammates who raise the bar and make us better. Always hire up, never down.

We partner with organizations of all sizes to explore, design, and implement AI strategies that are secure, scalable, and human-centered. We believe AI should amplify human potential, not replace it and we build with that conviction in every engagement. From advisory and tooling to implementation and education, we meet clients where they are at and help them integrate AI in ways that align with their mission and values. Our goal is to empower teams to work smarter, move faster, and unlock new possibilities through thoughtful, responsible innovation.

And through it all, we lead with purpose, love, and adventure. We do meaningful work with people we care about, and we make the ride an adventure worth taking. Because at Human Agency, who we are and how we work are one and the same.

The Opportunity

At Human Agency, we are shaping the future by combining cutting-edge AI solutions with intuitive software development. We specialize in building scalable and secure SaaS applications that empower enterprise clients to solve complex problems efficiently. As part of our team, you'll work on high-impact projects at the intersection of AI, machine learning, and secure software engineering.

We’re seeking a Senior Full-Stack Engineer to join our growing team. In this role, you'll lead development initiatives, contribute to our SaaS products, and tackle exciting challenges in backend architecture, frontend design, and AI/ML integrations with a particular focus on generative AI technologies.

Responsibilities

SaaS Application Development

• Detection:
• Develop backend algorithms to process articles, chunk content, and integrate with LLMs for predictive text generation.
• Build APIs and services to manage and scale the application’s content workflow.
• Protection:
• Create secure PDF upload workflows with encryption to ensure compatibility with Adobe Acrobat.
• Integrate authentication mechanisms like Okta for enterprise customer admin management.
• Enable data recovery, auditing capabilities, and enterprise-grade administrative tools.

AI/ML and Algorithm Development

• Collaborate with AI/ML teams to operationalize models such as OCR (Optical Character Recognition) and generative AI-based solutions (e.g., GPT models).
• Implement RAG (Retrieval-Augmented Generation) use cases using vector databases and data embedding techniques to enhance application functionality.
• Design workflows for large-scale data processing that enable efficient AI integrations and create dynamic AI-first features, such as custom GPTs tailored to enterprise needs.

DevOps & Operationalization

• Establish CI/CD pipelines and formal release structures for software delivery.
• Conduct security evaluations to identify vulnerabilities and optimize performance across AI-driven SaaS applications.
• Manage infrastructure and workflows for custom AI solutions, including tools like Snowflake and LangChain.

Future-Focused Development

• Build integrations and proof-of-concept applications leveraging company-specific datasets and AI technologies.
• Evaluate and implement tools for classification, indexing, and secure data management that incorporate the latest in generative AI advancements.
• Support the development of enterprise-grade AI solutions, including internal AI models and custom LLMs.

Requirements

Core Technical Skills

• Proficiency in Python and Ruby on Rails, with significant backend experience.
• Experience in designing secure systems, including encryption workflows and document protection mechanisms.
• Hands-on expertise in integrating generative AI tools, such as OpenAI APIs, LangChain, or Hugging Face frameworks, into SaaS applications.
• Strong knowledge of RAG techniques, vector databases (e.g. Pinecone, Weaviate), and embedding pipelines.
• Ability to operationalize AI/ML models and build workflows for enterprise clients.
• Frontend development experience in modern frameworks (e.g, React, Vue), with the ability to assess and recommend appropriate tech stacks.

Nice-to-Have Skills

• Computer vision and OCR expertise, especially for processing and indexing complex document workflows.
• Experience with authentication systems (e.g., Okta) and enterprise administrative tools.
• Experience in DevOps, CI/CD pipelines, and secure release management.
• Background in security evaluations, vulnerability assessments, and tool integrations.

Other Requirements

• Strong testing background with experience in automated and manual testing.
• Ability to manage data recovery, audits, and compliance workflows.
• Strong problem-solving and analytical skills, with attention to detail in complex environments.
• Excellent communication skills and the ability to work collaboratively across teams.

Why Join Us?

• Be part of a cutting-edge team solving complex problems in AI and SaaS development.
• Work on impactful projects with enterprise clients and cutting-edge technologies.
• Grow your career in a collaborative, innovative, and supportive environment.

If you're excited about working on secure, scalable systems while integrating advanced AI and ML capabilities, we’d love to hear from you.

Equal Opportunity Commitment

Human Agency is an Equal Opportunity Employer. We value diverse backgrounds and strive to build an inclusive culture where everyone feels welcomed and empowered.

Location: Remote (US or Canada)
Type: US Applicants - Full-Time; Canadian Applicants - Independent Contractor

About Us

We’re scaling rapidly and have a growing pipeline of opportunities that demand exceptional talent across disciplines. Our mission is to bring on individuals, from creative producers to technical experts to entrepreneurial leaders, who can help us realize this next chapter of growth.

We are a company of doers. Leaders roll up their sleeves, teams work flat, and everyone contributes to what ships. Titles don’t insulate us from feedback or basics. We invite critique, learn quickly, and keep raising the bar. The best ideas win here, no matter where they come from, because clients trust us to deliver the strongest outcomes every time.

Our clients’ missions, products, and bottom lines are sacred. We immerse ourselves in their world, becoming stewards of their goals and partners in solving big problems. Every product, strategy, or asset we create must be both beautiful and functional; practical, usable, and designed for real-world impact.

Humans are our most valuable resource, and we only grow by hiring people who push us forward. Across strategy, engineering, design, data, and operations, we seek out teammates who raise the bar and make us better. Always hire up, never down.

We partner with organizations of all sizes to explore, design, and implement AI strategies that are secure, scalable, and human-centered. We believe AI should amplify human potential, not replace it and we build with that conviction in every engagement. From advisory and tooling to implementation and education, we meet clients where they are at and help them integrate AI in ways that align with their mission and values. Our goal is to empower teams to work smarter, move faster, and unlock new possibilities through thoughtful, responsible innovation.

And through it all, we lead with purpose, love, and adventure. We do meaningful work with people we care about, and we make the ride an adventure worth taking. Because at Human Agency, who we are and how we work are one and the same.

About the role

We’re hiring a Product Lead to join our team and embed directly inside one of our client organizations — a leader in design-build. This role is for a hands-on product builder who thrives on embedding with teams, understanding their workflows, and delivering AI-powered software that drives real value. 

This is not a “strategy-only” role. You’ll spend your time with users, engineers, and executives alike — uncovering needs, setting priorities, and getting solutions shipped. You’ll bring modern product practices into a complex, real-world industry, while raising the bar for excellence in how software gets built.

What you’ll do

Discovery & Prioritization

• Embed with client teams to observe workflows, run discovery, and translate insights into actionable software requirements.
  
  
• Develop and apply structured prioritization methods (balancing user value, business outcomes, and technical feasibility) to guide trade-offs.
  
  
• Keep teams focused on the highest-leverage opportunities, not just the loudest requests.
  
  

Delivery & Execution

• Scope projects with realistic estimates and clear milestones that deliver value early and often.
  
  
• Know how to ship a prototype, gain feedback, iterate to value, and release production-grade solutions.
  
  
• Lead small pods of engineers and operators to ship high-quality, on-time solutions.
  
  
• Facilitate leadership updates and reporting, ensuring execs are aligned and confident in progress.
  
  
• Above all, be able to roll up your sleeves to solve the problem in a way that advances us and the organization.
  
  

AI & Technical Fluency

• Work directly with APIs, data pipelines, and AI models to shape and validate solutions.
  
  
• Partner with engineers to design and ship AI-driven tools that fit seamlessly into existing workflows.
  
  
• Establish best practices for evaluating tools — weighing accuracy, usability, and long-term fit.
  
  
• Stay current on AI capabilities and help translate them into practical applications inside the client’s environment.
  
  

Culture & Excellence

• Raise the bar for product craft, from clear scopes to clean communication and transparent evaluations that anyone can quickly understand.
  
  
• Codify lessons into playbooks so the client builds lasting product capability.
  
  
• Build a culture of excellence: sharing wins, measuring outcomes, celebrating progress, and owning areas of improvement.
  
  

Minimum qualifications

• 8+ years of product management experience, ideally building tools for complex workflows or mission-critical environments.
  
  
• Proven ability to balance discovery and delivery — finding the right problems and shipping solutions that stick.
  
  
• Experience leading small engineering teams or pods with high accountability.
  
  
• Consulting mindset: able to embed inside client organizations, guide practices, and earn trust quickly.
  
  
• Excellent communicator who can flex for end users, engineers, and executives.
  
  
• Comfortable working with APIs and data pipelines — able to shape requirements, validate inputs, and build against structured data.
  
  

• Experience with modern data platforms, ideally Snowflake (or equivalent warehouses like BigQuery, Redshift).
  
  
• Able to make smart build choices with AI — knowing when to call APIs, when to use custom GPTs, and how to combine them into solutions that actually work for teams.
  
  
• Operates confidently in cloud environments, especially Microsoft Azure and AWS ecosystems.
  
  
• Bonus: familiarity with enterprise integrations (e.g. Tableau) to connect insights into user workflows.
  
  
• Above all, be someone that people admire, because you’re having fun doing great work.
  
  

Why this role is different

Most product roles optimize features in a SaaS app. This one is about embedding with real teams across architecture, engineering, and construction, understanding how they work, and building AI-powered tools that change how projects get designed and delivered in the real world. You’ll see your work land quickly, and you’ll set the playbook for how we approach product for years to come. 

Equal Opportunity Commitment

Human Agency is an Equal Opportunity Employer. We value diverse backgrounds and strive to build an inclusive culture where everyone feels welcomed and empowered.

About us

Layer Health was founded in 2023 by leading machine learning researchers from MIT and Harvard Medical School. We are building an AI layer that can accurately and scalably synthesize information from medical records, with the mission to reduce friction everywhere in healthcare. Our LLM-powered platform is solving chart review once and for all, across use cases. For health systems, our first product dramatically accelerates clinical registry abstraction in areas ranging from surgery and cardiology, to oncology. Our long term vision is for our AI layer to safely transform patient care and minimize unnecessary heartbreak. Layer Health’s diverse founding team brings expertise across machine learning, UI/UX, large language models, and medicine.

We’re seeking exceptional engineers to join us as early members. Together, we will create the AI layer that will redefine healthcare for the better.

Here’s a collection of articles about our product, mission, recent funding round, etc.

Job Description

In this role, you will lead the design and implementation of key parts of our platform. This role can be hybrid (2-3 days per week in office) in either our Boston or NYC office.

You can expect to:

• Develop the backend for new product features, including implementing new data models and new APIs.
• Re-architect our databases and data infrastructure for scalability, security and reliability.
• Implement integrations with customer databases (e.g., EHR backends) and partner cloud data warehouses (e.g., Snowflake).
• Design, build, and maintain scalable and performant APIs and microservices on our core backend infrastructure. 
• Develop reliable, scalable services and client libraries on large amounts of data in the cloud.
• Innovate and implement robust data models and infrastructure that cater to various technical and business constraints for regulated areas.
• Collaborate on a cross-functional team to design and ship new product features/experiences. 
• Cultivate a robust engineering and product culture that drives the company forward.

We look for:

• 5-7+ years of experience in backend and cloud platform software development, with the ability and flexibility to traverse the stack when necessary. 
• Experience in ingesting and processing massive amounts of cloud-native data, with a focus on building scalable data pipelines and infrastructure.
• Experience developing and maintaining performant, scalable, and data-centric enterprise software products.
• Fluency in one or more programming languages, e.g. Python, Java, C, C++, Rust, or Go. 
• Fluency with modern, cloud-native tech stacks and frameworks.
• Experience with infrastructure as code and cloud deployment frameworks like Kubernetes, Helm, and Terraform.
• A strong communicator who thrives in a customer-focused, fast-paced environment. 
• An excited and adaptable team player who wants to disrupt the healthcare industry with AI/ML, alongside an awesome team.

Expected compensation range for this role is $200,000-250,000, in addition to stock options. Compensation is dependent on experience, overall fit to our role, and candidate location. Expected compensation ranges for this role may change over time. If your compensation requirement is greater than our posted salary ranges, please still consider applying to our role. We will make a determination as to whether an exception can be made. 

If you are excited about this role, we encourage you to apply even if you don't feel that you meet every single requirement. We're eager to meet people that believe in our mission and can contribute to our team in a variety of ways. We welcome diverse perspectives, rigorous thinking, and fearlessness in challenging the status quo. 

Layer Health is committed to fostering an environment of inclusion that is free from discrimination.  We are an Equal Opportunity Employer where employment is decided on the basis of qualifications, merit, and business need. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected Veteran status, or any other characteristic protected by law.

Join us and help us transform healthcare with AI.