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.

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.

Senior Analytics Engineer, Finance

Key skills