Senior Analytics Engineer

Overview

Ready to be pushed beyond what you think you’re capable of?

At Coinbase, our mission is to increase economic freedom in the world. It’s a massive, ambitious opportunity that demands the best of us, every day, as we build the emerging onchain platform — and with it, the future global financial system.

We’re seeking a candidate who aligns with our mission and believes in the power of crypto and blockchain technology to update the financial system. We value initiative, a drive to learn, and collaboration with high-caliber colleagues to solve hard problems.

Our work culture is intense and not for everyone. If you want to build the future alongside others who excel and expect the same from you, Coinbase is a strong fit.

While many roles at Coinbase are remote-first, this role requires in-person participation throughout the year. Team and company-wide offsites are held multiple times annually to foster collaboration and alignment. Attendance is expected and fully supported.

The Analytics Engineering team bridges data engineering, data science, and business analytics by building scalable, impactful data solutions. We transform raw data into actionable insights through robust pipelines, well-designed data models, and tools that empower stakeholders to make data-driven decisions. We prioritize data quality, reliability, and usability so stakeholders can rely on our data to drive outcomes.

• Trusted Data Sources:
  Develop and maintain foundational data models that serve as the single source of truth for analytics across the organization.
• Actionable Insights:
  Translate business requirements into scalable data models, dashboards, and tools.
• Cross-Functional Collaboration:
  
  Partner with engineering, data science, product, and business teams to align on priorities and data solutions.
• Scalable Data Products:
  Build frameworks, tools, and workflows that maximize efficiency for data users while maintaining data quality and performance.
• Outcome-Focused Solutions:
  Use modern development and analytics tools to deliver value quickly while ensuring long-term maintainability.

The analytics engineer is a hybrid Data Engineer/Data Scientist/Business Analyst role with end-to-end data understanding and an engineering toolkit to extract value through building tables and enabling insights.

• Be the expert:
  Quickly build subject matter expertise in a specific business area and data domain. Understand data flows from creation, ingestion, transformation, and delivery.
• Examples:
• Step into a new line of business and work with Engineering and Product partners to deliver first data pipelines and insights.
• Communicate with engineering teams to fix data gaps for downstream data users.
• Take initiative and accountability for fixing issues anywhere in the stack.
• Generate business value:
  Interface with stakeholders on data and product teams to deliver commercial value from data (directly or indirectly).
• Examples:
• Build out a new data model enabling multiple downstream DS teams to unlock business value through experimentation and ad hoc analysis.
• Combine engineering details of the algorithm engine with statistics and data expertise to propose feasible solutions for engineering to improve the algo.
• Work with PMs to tie together new x-PG and x-Product data into one holistic framework to optimize key financing product metrics.
• Focus on outcomes, not tools:
  Use a variety of frameworks to identify the best-fit tools to deliver value.
• Examples:
• Develop new abstractions (e.g., UDFs, Python packages, dashboards) to support scalable data workflows/infra.
• Stand up a framework for building data apps internally, enabling other DS teams to quickly add value.
• Demonstrate mastery of established tools (e.g., SQL) to deliver impact when speed is the priority.

In addition to out-of-the-box thinking, attention to detail, a sense of urgency, and a high degree of autonomy and accountability, we expect the following skills:

• Data Modeling Expertise:
  Strong understanding of modular and reusable data models (e.g., star and snowflake schemas).
• Prompt Design and Engineering:
  Expertise in prompt engineering for LLMs, including…