Senior Analytics Engineer (Platform - Financial Analytics)

Coinbase

2d•$180,370 - $212,200•Remote

About The Position

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.

Requirements

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.

Responsibilities

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.