Analytics Engineer

Coinbase

2d•$152,405 - $179,300•Remote

About The Position

As an Analytics Engineer on the Compliance Data team within Platform, you'll build and maintain the production-grade data foundations that every regulatory obligation at Coinbase depends on. This team owns the Compliance Data Mart (CDM), Coinbase's canonical source of truth for user, transaction, and compliance data. You'll own the full lifecycle of data models and pipelines that downstream reporting, analytics, and AI workflows all build on, while also delivering accurate, timely data when regulators come calling.

Requirements

2+ years of experience building and maintaining production data pipelines and data models, with strong proficiency in SQL, Python, dbt, and a modern warehouse platform (Snowflake, Databricks, or similar).
Track record implementing data quality frameworks including data contracts, validation logic, reconciliation, and monitoring to catch issues before they reach downstream reports.
Experience supporting time-sensitive, high-stakes data needs (regulatory exams, audits, or financial reporting) where accuracy and timeliness are non-negotiable.
Demonstrated ability to turn tribal knowledge and recurring manual workflows into durable, documented infrastructure.
Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.

Responsibilities

Own end-to-end development of production-grade data models and pipelines at the core of the CDM, covering user, transaction, and compliance data across retail and institutional product lines, from architecture through deployment and maintenance.
Build data quality checks, data contracts, validation logic, and monitoring that protect data integrity before issues propagate to downstream reports or workflows.
Partner with upstream engineering teams to fix data gaps and absorb product changes at the source, taking accountability for data issues anywhere in the stack rather than passing them downstream.
Support live regulatory exams, audits, and ad hoc regulator requests with accurate, timely data, balancing time-sensitive reactive work with long-term foundational projects.
Automate recurring manual workflows into scalable pipelines and self-serve tooling, enabling downstream teams and AI agents to answer their own questions.