Staff Analytics Engineer

Harvey • San Francisco, CA

34d • $200,000 - $250,000

About The Position

At Harvey, we’re transforming how legal and professional services operate — not incrementally, but end-to-end. 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. With 700+ customers in 58+ countries, 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 is sharp, motivated, and deeply committed to the mission. We move fast, operate with intensity, and take real ownership of the problems we tackle — from early thinking to long-term outcomes. We stay close to our customers — from leadership to engineers — and work together to solve real problems with urgency and care. If you thrive in ambiguity, push for excellence, and want to help shape the future of work alongside others who raise the bar, we invite you to build with us. At Harvey, the future of professional services is being written today — and we’re just getting started. Role Overview We’re looking for a Staff 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 founding 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. 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.

Requirements

• 7+ years of experience in Analytics Engineering, Data Engineering, or Data Science
• 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.

Responsibilities

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 251-500 employees