The opportunity
Ready to do the most impactful work of your career? At Coinbase , we are uncompromising on our mission to increase economic freedom.
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.
+ 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.
What they're looking for
- 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.
