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 believes in the power of crypto and blockchain technology to update the financial system. We value someone who relishes pressure, seeks feedback to level up, and will tackle the company’s hardest problems. Our work culture is intense and not for everyone, but it’s the best place to build the future alongside high-caliber colleagues.

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 supported.

The CX Analytics Engineering team bridges data engineering, data science, and business analytics by building scalable data solutions. We transform raw data into actionable insights through robust pipelines, 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 influence the overarching strategy for CX Analytics and its partners.

Our team combines technical expertise with business understanding to unlock the full potential of our data. We prioritize data quality, reliability, and usability to drive meaningful outcomes.

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

Responsibilities

• 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.
• Generate business value:
  Interface with stakeholders on data and product teams to deliver commercial value from data.
• Focus on outcomes:
  Use frameworks to identify the best-fit tools to deliver value. Develop abstractions (e.g., UDFs, Python packages, dashboards) to support scalable data workflows/infra.

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.
• Build out a new data model enabling downstream teams to unlock business value through experimentation and ad hoc analysis.
• Combine engineering details of the algorithm engine with statistics and data expertise to improve the algorithm.
• Work with PMs to unify new data into a holistic framework to optimize key financing product metrics.

Note:

Other duties as assigned may be included as part of the role.

What you’ll be doing (detailed):

Analytics engineer is a hybrid role with the expertise to understand data flows end to end and the engineering toolkit to maximize value indirectly (building tables) or directly (solving problems, delivering insights).

In addition to out-of-the-box thinking, attention to detail, a sense of urgency, and autonomy, the following skills are expected:

• Customer Support Data
  
  Experience:
  
  Familiarity with data elements and processes for Customer Support initiatives, including employee performance monitoring, workforce inputs, and handling of sensitive PII across a broad stakeholder base.
• Data Modeling Expertise:
  Strong understanding of modular and reusable data models (e.g., star/snowflake schemas).
• Prompt Design and Engineering:
  Expertise in prompt engineering for LLMs, including creating, refining, and optimizing prompts for internal tools.
• Advanced SQL:
  Proficiency in advanced SQL techniques for data transformation, querying, and optimization.
• Intermediate to Advanced Python: OOP, scripting, automation, and building scalable frameworks.
• Collaboration and Communication:
  Ab…