Staff Analytics Engineer, Compliance Data

Coinbase is a remote-first company focused on increasing economic freedom. They are hiring a Staff Analytics Engineer to lead the data foundations for the Compliance Data team, ensuring accurate and timely data for regulatory reporting while defining the technical direction for compliance data modeling and validation.

Responsibilities:

• Own the systems, architecture, technical strategy, and roadmap for the Compliance Data, including the data models, pipelines, quality frameworks, and certification processes that the entire compliance reporting stack depends on
• Build production-grade solutions, in addition to data models and pipelines, covering user, transaction, and compliance data across retail and institutional product lines, designing for durability, auditability, and consumption by both analysts and AI agents
• Drive data quality at the source by establishing data contracts, validation frameworks, monitoring, and reconciliation logic that catch issues before they propagate to downstream reports or regulatory filings
• Partner with upstream engineering teams, Compliance leadership, and downstream consumers to close data gaps, absorb product changes proactively, and define the standards that make compliance data trustworthy and self-serve
• Lead the team's response to live regulatory exams, audits, and time-sensitive data requests, ensuring accuracy and timeliness under pressure while building repeatable processes that reduce future manual effort
• Shape engineering standards and technical culture across the Compliance Data team through architectural decisions, code reviews, mentorship, and converting tribal knowledge into durable, documented infrastructure

Requirements:

• 8+ years of experience in analytics engineering, data engineering, or data-intensive technical roles, with demonstrated technical leadership across teams or domains
• Expert-level SQL and Python proficiency applied to production pipeline development, complex transformations, data quality automation, and orchestration frameworks (dbt, Airflow, or equivalent)
• Track record of designing and owning systems and canonical or certified data models that serve multiple downstream teams, with deep fluency in dimensional modeling, data contracts, and modern warehouse architecture (Snowflake, Databricks)
• Proven ability to lead under regulatory pressure, including delivering accurate data for exams, audits, or filings where errors have material consequences
• History of driving technical direction and influencing engineering practices beyond your immediate team, including scoping complex projects, making independent architectural decisions, and mentoring other engineers
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality