Senior Analytics Engineer, Harvey

• If you combine engineering rigor with a love of storytelling through data—and want to shape analytics from the ground up—we’d love to meet you
• 5+ years of experience in Analytics Engineering, Data Engineering, Data Science, or similar field
• Deep expertise in SQL, dbt, Python, and modern BI/semantic layer tools like Looker or Omni
• 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 operating in a B2B or commercial setting, with an understanding of customer lifecycle and revenue-driving metrics

Desirable

• We’re looking for a versatile Senior Analytics Engineer to architect the data backbone that powers 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 immediately usable insights
• As the first Analytics Engineer on our team, you’ll choose and implement the right 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
• 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) 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 real-time business health metrics
• Establish and champion data modeling standards and best practices, guiding the organization in how to model data for accuracy, usability, and long-term maintainability
• Collaborate with engineering to make key decisions on data architecture, co-design data schemas, and implement orchestration strategies that ensure reliability and performance of the data warehouse
• Lead data governance initiatives, ensuring high standards of data quality, consistency, documentation, and access control across the analytics ecosystem
• Empower stakeholders with data by making analytical assets easily discoverable, reliable, and well-documented—turning complex datasets into actionable insights for the business