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.

To achieve our mission, we’re seeking a very specific candidate. We want someone 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 on the world, who relishes the pressure and privilege of working with high caliber colleagues, and who actively seeks feedback to keep leveling up.

We want someone who will run towards, not away from, 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 and expect the same from you, 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. Team and company-wide offsites are held multiple times annually to foster collaboration, connection, and alignment. Attendance is expected and fully supported.

Analytics engineer is a hybrid Data Engineer/Data Scientist/Business Analyst role that has the expertise to understand data flows end to end, and the engineering toolkit to extract the most value out of it 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 the 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 out a new data model allowing multiple downstream DS teams to more easily unlock business value through experimentation and ad hoc analysis. Combine Eng details of the algo engine with stats and data expertise to come up with feasible solutions for Eng to make the algo better. Work with PMs to tie together new x-PG, and x-Product data into one holistic framework to optimize key financing product business metrics.

• Focus on outcomes not tools: Use a variety of frameworks and paradigms 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. Use established tools with mastery to quickly deliver impact when speed is top priority.

What We Look For in You

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 you to have the following skills:

• Customer Support Data
  
  Experience:
  
  Familiarity with data elements and processes supporting successful Customer Support initiatives, including employee performance monitoring, workforce/staffing inputs, and the handling of sensitive PII across a broad stakeholder base.

• Data Modeling Expertise
  :
  Strong understanding of best practices for designing modular and reusable data models (e.g., star schemas, snowflake schemas).

• Prompt Design and Engineering: Expertise in prompt engineering and design for LLMs (e.g., GPT), including creating, refining, and optimizing prompts to improve response accuracy, relevance, and performance for internal tools and use cases.

• Advanced SQL
  :
  Proficiency in advanced SQL techniques for data transformation, querying, and optimization.

• Intermediate to Advanced Python
  :
  Expertise in scripting and automation, with experience in Object-Oriented…