Job description

WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW We’re looking for a versatile Senior Analytics Engineer focusing on Product to architect event data models that power decision-making at Harvey. With product-market fit already proven and demand surging across diverse customer segments, you’ll design clean, reliable pipelines and semantic data models that turn raw events into usable insights. As an Analytics Engineer on our team, you’ll help evolve our data stack, champion best practices in testing and documentation, and collaborate closely with product, GTM, and leadership to ensure every team can answer its own questions with confidence. If you combine engineering rigor with a love of storytelling through data we’d love to meet you. WHAT YOU'LL DO - Design and build scalable data models and pipelines using dbt to transform raw data into clean, reliable assets that power company-wide analytics and decision-making. - Define and implement a robust semantic layer (e.g. LookML/Omni/Other) that standardizes key business metrics, dimensions, and data products, ensuring self-serve capabilities for stakeholders across teams. - Partner cross-functionally with Product, GTM, Finance, and the Exec Team to deliver intuitive, consistent dashboards and analytical tools that surface business health metrics. - Establish and champion data modeling standards and best practices, guiding the organization in how to model data for accuracy, performance, usability, and long-term maintainability. - Partner with Product Managers, Engineers, and Data teams to design tracking plans for new product surfaces, ensuring events are implemented accurately, consistently, and with downstream analytics use cases in mind. - Own the product event tracking strategy, including event naming conventions, property schemas, identity resolution, sessionization, versioning, deprecation, and documentation standards. - Empower stakeholders with data by making analytical assets easily discoverable, reliable, and well-documented – turning complex datasets into actionable insights for the business. - You’ll define the structure, taxonomy, governance, and modeling patterns for product event data, ensuring that user behavior, product usage, and customer journeys are captured consistently from instrumentation through analytics-ready models. WHAT YOU HAVE - 5+ years of experience in Analytics Engineering, Data Engineering, Data Science, or similar field. - Deep expertise in SQL, dbt, Python, Snowflake. - Experience with modern BI tools like (Looker/Omni, or similar). - Skilled at defining core business and product metrics, uncovering insights, and resolving data inconsistencies across complex systems. - Strong familiarity with version control (GitHub), CI/CD, and modern development workflows. - Bias for action – you prefer launching usable, iterative data models that deliver immediate value over waiting for perfect solutions. - Strong communicator who can build trusted partnerships across Product, GTM, Finance, and Exec stakeholders. Comfortable working through ambiguity in fast-moving, cross-functional environments. - Balances big-picture thinking with precision in execution – knowing when to sweat the details and when to move quickly. - Experience modeling high-volume, semi-structured product event data, including JSON payloads, nested properties, user/account identifiers, sessions, funnels, cohorts, and behavioral metrics. - Experience with product analytics tools (Mixpanel, Segment, Amplitude) BONUS - Early employee at a hyper-growth startup - Experience with or knowledge of AI and LLMs - Data Engineering Experience - Experience managing data warehouse (preferably Snowflake) - Experience at world-class enterprise orgs (ex: Brex, Ramp, Stripe, Palantir) COMPENSATION $155,800 - $233,600 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES [HERE notion.so]. #LI-CD1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Requirements

Must-haves

At Harvey, the future of professional services is being written today — and we’re just getting started.
Design and build scalable data models and pipelines using dbt to transform raw data into clean, reliable assets that power company-wide analytics and decision-making.
Define and implement a robust semantic layer (e.g. LookML/Omni/Other) that standardizes key business metrics, dimensions, and data products, ensuring self-serve capabilities for stakeholders across teams.
Partner cross-functionally with Product, GTM, Finance, and the Exec Team to deliver intuitive, consistent dashboards and analytical tools that surface business health metrics.
Establish and champion data modeling standards and best practices, guiding the organization in how to model data for accuracy, performance, usability, and long-term maintainability.
Partner with Product Managers, Engineers, and Data teams to design tracking plans for new product surfaces, ensuring events are implemented accurately, consistently, and with downstream analytics use cases in mind.
Own the product event tracking strategy, including event naming conventions, property schemas, identity resolution, sessionization, versioning, deprecation, and documentation standards.
Empower stakeholders with data by making analytical assets easily discoverable, reliable, and well-documented – turning complex datasets into actionable insights for the business.
5+ years of experience in Analytics Engineering, Data Engineering, Data Science, or similar field.
Deep expertise in SQL, dbt, Python, Snowflake.
Experience with modern BI tools like (Looker/Omni, or similar).
Skilled at defining core business and product metrics, uncovering insights, and resolving data inconsistencies across complex systems.
Strong familiarity with version control (GitHub), CI/CD, and modern development workflows.
Bias for action – you prefer launching usable, iterative data models that deliver immediate value over waiting for perfect solutions.
Strong communicator who can build trusted partnerships across Product, GTM, Finance, and Exec stakeholders.
Comfortable working through ambiguity in fast-moving, cross-functional environments.
Balances big-picture thinking with precision in execution – knowing when to sweat the details and when to move quickly.
Experience modeling high-volume, semi-structured product event data, including JSON payloads, nested properties, user/account identifiers, sessions, funnels, cohorts, and behavioral metrics.
Experience with product analytics tools (Mixpanel, Segment, Amplitude)

Nice-to-haves

Early employee at a hyper-growth startup
Experience with or knowledge of AI and LLMs
Data Engineering Experience
Experience managing data warehouse (preferably Snowflake)
Experience at world-class enterprise orgs (ex: Brex, Ramp, Stripe, Palantir)

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aisqlpythonci/cd

Platform: ashby
Effort: quick