Senior Analytics Engineer, Finance

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 financial analytics and decision-making.
• Define and implement a robust semantic layer (e.g. LookML/Omni/Other) that standardizes financial and operating metrics, including revenue, retention, customer growth, usage, margin, and forecast inputs.
• Partner cross-functionally with Product, Finance, and the Exec Team to deliver intuitive, consistent dashboards and analytical tools that surface business health metrics (ARR, NRR).
• 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.
• Lead data governance initiatives, ensuring high standards of data quality, consistency, documentation, and access control across the analytics ecosystem.
• Structure financial metric definitions, business logic, and accounting context in ways that can support AI-assisted reporting, natural language analytics, and automated anomaly detection.

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 financial and operating 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 Finance, GTM, Product, 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 financial, billing, subscription, CRM, or usage-based revenue data.
• Strong understanding of business metrics such as ARR, MRR, churn, retention, expansion, bookings, billings, and revenue recognition.

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)