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.

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.

• 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.