Compliance Technology Senior Analyst

Coinbase, Inc.; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

Coinbase, Inc. is seeking an Analytics Engineer to join the Compliance Technology team within Platform. You will build end-to-end data pipelines powering compliance detection and decisioning, while ensuring data quality, lineage, and audit readiness.

You will curate datasets and models for Transaction Monitoring, EDD/KYC, and Screening systems, partnering with Compliance and Engineering to close data gaps and drive measurable improvements in signal quality and operational efficiency.

Qualifications

• Advanced SQL proficiency with complex transformations and data quality checks.
• Experience building and maintaining ETL/ELT pipelines and data models using dbt, Airflow, or equivalent tools.
• Minimum 5 years in analytics engineering, data engineering, or data-heavy technical roles.
• Responsible use of generative AI with human oversight to improve workflows and outputs.
• Proficient Python for automation, pipeline development, and data analysis.
• Ability to drive cross-functional projects in regulated environments with clear communication.

Responsibilities

• Build and own end-to-end data pipelines powering compliance detection and decisioning.
• Own datasets and data models for compliance controls across TM, EDD/KYC, and Screening systems.
• Improve data quality, lineage, validation, and documentation for audit readiness.
• Collaborate with Compliance, Product, and Engineering to close upstream data gaps.
• Define and measure control performance and successful rollout metrics.
• Develop repeatable reporting pipelines for Compliance leaders and auditors.

Skills

Advanced SQL

Python

Cross-functional collaboration

Tools

dbt

Airflow

Job description

• 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
• 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

• 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
• Minimum 5 years of experience in analytics engineering, data engineering, or data-heavy technical roles
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality
• Python skills applied to automation, pipeline development, and data analysis
• Demonstrated ability to execute on ambiguous, cross-functional projects in regulated environments with clear written and verbal communication