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 is passionate about our mission and who believes in the power of crypto and blockchain technology to update the financial system. We want someone who is eager to leave their mark, who thrives under pressure, and who actively seeks feedback to level up. We want someone who will run toward solving the company’s hardest problems.

Our work culture is intense and isn’t for everyone. If you want to build the future alongside others who excel in their disciplines, there’s no better place to be. While many roles at Coinbase are remote-first, we are not remote-only. In-person participation is required throughout the year, with team and company-wide offsites held multiple times annually to foster collaboration, connection, and alignment.

Attendance is expected and supported.

The CX 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. As an Analytics Engineer, you will enable Analytics and Operations to function at scale and translate complex technical and operational requirements into easily consumable front-end data solutions, while influencing the overarching strategy for CX Analytics and its partners.

We prioritize data quality, reliability, and usability to ensure stakeholders can rely on data to drive meaningful 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 for stakeholders.
• Cross-Functional Collaboration:
  
  Partner with engineering, data science, product, and business teams to align on data solutions and priorities.
• 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 with long-term maintainability.

Analytics engineer is a hybrid Data Engineer/Data Scientist/Business Analyst role with end-to-end data flow understanding and the engineering toolkit to extract value indirectly (building tables) or directly (solving problems, delivering insights).

Expectations

• 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 the most commercial value from data (directly or indirectly).

Examples:

• Build a new data model allowing multiple downstream DS teams to unlock value through experimentation and ad hoc analysis.
• Combine engineering details of the algo engine with stats and data expertise to improve the algo.
• Work with PMs to integrate new x-PG and x-Product data into one holistic framework to optimize key financing product business metrics.

Focus on outcomes not tools:
Use various frameworks to identify the best-fit tools to deliver value.

Examples:

• Develop abstractions (e.g., UDFs, Python packages, dashboards) to support scalable data workflows/infra.
• Stand up a framework for building data apps internally to enable other teams to quickly add value.
• Use established tools (e.g., SQL) to deliver impact when speed is top priority.