Senior Analytics Engineer, Harvey

• 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)

Desirable

• 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
• 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