We're hiring an Analytics Engineer to join the Compliance Technology team within the Platform organization. This team builds and operates data and detection capabilities that enable Coinbase to meet global BSA/AML and related regulatory obligations at scale, partnering with Compliance, Product, and Engineering to ensure monitoring and investigative controls are effective, measurable, and auditable. You'll build and own end-to-end data pipelines that power compliance detection and decisioning, improving signal quality, defining control performance measurement, and creating scalable tooling that makes compliance data easier to trust and use.

What you'll do:

• Build and maintain curated datasets and data models (e.g., case, alert, entity, transaction, and risk features) that power compliance controls across Transaction Monitoring, EDD/KYC, and Screening systems.
• Own data quality for compliance datasets by implementing validation checks, monitoring, lineage tracking, and documentation to ensure outputs are accurate, complete, and audit-ready.
• Partner with cross-functional engineering teams and Compliance stakeholders to close upstream data gaps, standardize instrumentation, and translate control requirements into scalable data structures and feature sets.
• Drive detection signal quality improvements by analyzing detection outputs, identifying opportunities to reduce non-actionable noise while protecting sensitivity, and defining success metrics for controlled rollouts.
• Execute measurement frameworks for compliance control effectiveness, including coverage, timeliness, stability, and outcome-based metrics, and build repeatable reporting pipelines consumable by Compliance leaders, operations teams, and auditors.

Required skills and experience:

• Minimum 5 years of experience in analytics engineering, data engineering, or data-heavy technical roles.
• Advanced SQL proficiency including complex transformations, performance tuning, and data quality checks.
• Experience building and maintaining ETL/ELT pipelines and data models using tools such as dbt, Airflow, or equivalent orchestration/transformation frameworks.
• Demonstrated ability to execute on ambiguous, cross-functional projects in regulated environments with clear written and verbal communication.
• Python skills applied to automation, pipeline development, and data analysis.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.