Analytics Engineer

Why Join Coinbase:
We’re here to build a more open and free financial system through crypto and blockchain—and we’re looking for people who believe in that mission. This isn’t just a job; it’s a chance to make a global impact alongside top talent.
If you thrive under pressure, welcome honest feedback, and want to take on big challenges with a high-performing team, you’ll feel right at home here. It’s fast-paced and demanding—but also meaningful, and we wouldn’t have it any other way.

About the Role:
The CX Analytics Engineering team turns raw data into insights that drive smarter decisions across the company. We build scalable pipelines, clean data models, and tools that help teams move faster and work smarter.
As an Analytics Engineer, you’ll be the connector between data, tech, and operations—building front-end solutions and helping shape the strategy behind CX Analytics. It’s a mix of hands-on engineering and big-picture thinking, with a strong focus on data quality, usability, and impact.

What We Do:

• Build reliable data models that serve as a single source of truth across the company.
• Turn business needs into scalable dashboards, tools, and insights teams can act on.
• Work closely with engineering, product, and ops to stay aligned and deliver the right solutions.
• Create tools and workflows that boost efficiency without compromising data quality.
• Use modern analytics stacks to move fast—while keeping things maintainable long-term.

What you’ll be doing (ie. job duties):
An analytics engineer is a hybrid Data Engineer/Data Scientist/Business Analyst that has the expertise to build tables, solve problems, and turn complex data flows into valuable insights.

Expectations:
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.

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.

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 (e.g. Google Sheets, SQL) to quickly deliver impact when speed is top priority

What we look for in you:
In addition to creative thinking, attention to detail, a sense of urgency, and strong ownership, the following skills are expected:

• Strong understanding of modular and reusable data model design, including star and snowflake schemas.
• Proven expertise in prompt engineering for LLMs, with experience designing, refining, and optimizing prompts.
• Advanced proficiency in SQL for data transformation, querying, and performance optimization.
• Ability to translate complex technical concepts into business value across cross-functional teams.
• Hands-on experience developing and maintaining ETL/ELT pipelines using tools like dbt or Airflow.
• Familiarity with Git, CI/CD, and modern software development workflows.
• Solid grasp of modern data lake and warehouse architectures, such as Snowflake or Databricks.
• Strong business acumen with a focus on solving challenges through analytics engineering.

Nice to have:

• Proficiency in Python for scripting and automation, with experience in OOP and building scalable frameworks.
• Skill in developing polished dashboards using tools like Looker, Tableau, Superset, or Python libraries such as Matplotlib or Plotly.
• Experience working with cloud platforms like AWS or GCP.
• Comfort with core statistical concepts and a strong grasp of probability.

Compensation & Benefits:
Total compensation includes a base salary (varies by location), plus target bonus, equity, and full benefits—medical, dental, vision, and 401(k).
Pay Range: $122,000—$147,000 USD

Equal Opportunity:
We’re proud to be an Equal Opportunity Employer. All qualified applicants will be considered—regardless of race, gender, background, or identity. We also follow all laws regarding fair hiring practices, including E-Verify and consideration of applicants with criminal histories.