Senior Analytics Engineer

As a Senior Analytics Engineer on the Platform team, you'll build the scalable data models and pipelines that power analytics, experimentation, and decision-making across Coinbase. Our Analytics Engineering team transforms raw data into trusted, well-modeled sources that stakeholders across Product, Engineering, and Data Science rely on daily. You'll own end-to-end data solutions for specific business domains, turning complex data flows into clean, reusable frameworks that unlock commercial value at scale.

What you'll do:

• Own end-to-end data modeling for assigned business domains, from understanding source system data flows through designing modular, reusable models (star/snowflake schemas) that serve as the single source of truth for downstream teams.
• Build and optimize ETL/ELT pipelines using modern tools like dbt and Airflow, ensuring data quality, reliability, and performance at scale across Snowflake or similar warehouse architectures.
• Partner with Engineering, Product, and Data Science teams to identify data gaps, define requirements, and deliver data products that directly enable experimentation, ad hoc analysis, and business metric optimization.
• Develop scalable abstractions and frameworks (UDFs, Python packages, internal data apps) that multiply the efficiency of other data teams and reduce time-to-insight across the organization.
• Design and deliver dashboards and visualization layers using tools like Looker or Tableau, translating complex data into clear, actionable views for cross-functional stakeholders.

Required Skills and Experience:

• 5+ years in analytics engineering or data engineering with demonstrated expertise in designing modular data models and building production ETL/ELT pipelines using dbt, Airflow, or similar.
• Advanced SQL proficiency for complex transformations and query optimization, plus intermediate-to-advanced Python for scripting, automation, and building scalable frameworks (OOP experience preferred).
• Production experience with modern data warehouse architectures (Snowflake, Databricks) including performance tuning, data quality monitoring, and version-controlled development workflows (GitHub, CI/CD).
• Proven track record delivering data solutions that generated measurable business impact, with the ability to independently build domain expertise and communicate technical trade-offs to Product, Engineering, and business stakeholders.
• Experience with prompt engineering for LLMs (e.g., GPT), including designing and optimizing prompts for internal tooling and automation use cases.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.