Description
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.
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 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.
Requirements
- 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 Have
- 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
- Offers Equity
- Offers Bonus
- Comprehensive health, dental and vision coverage
- Retirement benefits (401k match up to 4%)
- Flexible PTO