Staff Analytics Engineer

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.

What we look for in you:

• Accounting exposure, either formal (accounting/finance degree or coursework, CPA/CMA) or hands-on work with Accounting/FP&A teams, such as supporting month-end close, reconciliations or financial reporting.
• Expert SQL (window functions, complex aggregations, optimization) and strong Python (OOP, reusable frameworks). No production ETL or SQL experience = reject.
• Deep data modeling: designed modular, reusable source-of-truth models (star/snowflake schemas, SCDs) used by many teams.
• Built and owned production ETL/ELT pipelines with dbt, Airflow or similar, including testing, monitoring and SLAs.
• Modern data warehouse/lakehouse experience (Snowflake, Databricks, BigQuery, Redshift). Snowflake is not required; no comparable warehouse experience = reject.
• Staff-level scope: led multi-quarter, cross-team technical initiatives, set architecture/direction, mentored engineers.