Staff Analytics Engineer

📍 United States

USD 207,500-244,100 per year

SENIOR

✅ Remote

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

Airflow @ 4 Audit Change Management Data Modeling @ 7 Databricks @ 4 ELT @ 4 ETL @ 4 LLM Mentoring @ 4 Python @ 7 Reporting @ 6 SQL @ 4 Snowflake @ 7 dbt @ 4

Details

Coinbase's Finance Analytics team builds financial data and reporting foundations for Finance, Accounting/Controllership, Treasury, and FP&A. The team owns source-of-truth financial datasets in Snowflake and Databricks, the pipelines that produce them, and reconciliation, month-end close, safeguarding, and reporting workflows. The team partners closely with Product Engineering to ensure upstream financial data is complete, reconciled, and traceable.

Coinbase is remote-first but not remote-only. Employees are expected to meet quarterly for in-person working sessions called “surges.”

Responsibilities

Partner with Accounting and FP&A to support month-end and quarter-end close by building datasets, reconciliations, and reporting that enable accurate and timely financial close.
Make financial data and pipelines audit-ready through 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 or Databricks that are correct, consistent, and reconciled with the general ledger.
Build and own scalable batch and, where needed, near-real-time pipelines orchestrated with Airflow and dbt, including dependency management, backfills, and runbooks.
Define reusable data quality and reconciliation frameworks, including tests, monitoring, alerting, and SLAs, and establish financial correctness criteria with Accounting.
Drive data contracts with upstream engineering teams.
Mentor Analytics Engineers.
Build abstractions, including Python packages, UDFs, and LLM-assisted tools, to improve the efficiency of finance data users.

Requirements

Accounting exposure through an accounting or finance degree, coursework, CPA/CMA certification, or hands-on work with Accounting or FP&A teams, such as supporting month-end close, reconciliations, or financial reporting.
Expert SQL skills, including window functions, complex aggregations, and optimization.
Strong Python skills, including object-oriented programming and reusable frameworks.
Production ETL or SQL experience.
Deep data modeling experience, including designing modular and reusable source-of-truth models, star and snowflake schemas, and slowly changing dimensions (SCDs).
Experience building and owning production ETL/ELT pipelines with dbt, Airflow, or similar tools, including testing, monitoring, and SLAs.
Experience with a modern data warehouse or lakehouse such as Snowflake, Databricks, BigQuery, or Redshift.
Staff-level experience leading multi-quarter, cross-team technical initiatives, setting architecture and technical direction, and mentoring engineers.

Compensation

The annual base salary range is $207,485–$244,100 USD, excluding equity and bonus. Total compensation may also include equity, bonus eligibility, and medical, dental, vision, and 401(k) benefits.