Staff Analytics Engineer

Ready to do the most impactful work of your career? At Coinbase , we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” learn more about working at Coinbase . Finance Analytics builds Coinbase's financial data and reporting foundations so Finance, Accounting/Controllership, Treasury and FP&A can run accurate, timely and auditable operations. We sit at the intersection of data engineering, data science and business analytics: we own the source-of-truth financial datasets in Snowflake/Databricks, the pipelines that produce them, and the reconciliation, month-end close, safeguarding and reporting workflows built on top. We partner closely with Product Engineering to make sure upstream financial data is complete, reconciled and traceable.

What you'll be doing:

As the Staff Analytics Engineer on Finance Analytics, this person sets the technical direction for Coinbase's financial data layer and works side by side with Accounting, Controllership and FP&A. They need to understand how the books are closed, audited and controlled, not just how the data moves: Partner with Accounting and FP&A to support month-end and quarter-end close, building the datasets, reconciliations and reporting that let us close the books accurately and on time. Make financial data and pipelines audit-ready: SOX-compliant controls, lineage, change management and evidence for internal and external auditors. Design and own the architecture of curated, well-documented source-of-truth financial datasets in Snowflake/Databricks that are correct, consistent and tie out to the general ledger. Build and own scalable batch (and where needed, near-real-time) pipelines orchestrated with Airflow/dbt, including dependency management, backfills and runbooks. Define reusable data quality and reconciliation frameworks (tests, monitoring, alerting, SLAs) and agree with Accounting on what "done" means for financial correctness. Drive data contracts with upstream engineering teams, mentor Analytics Engineers, and build abstractions (Python packages, UDFs, LLM-assisted tools) that make finance data users more efficient.