Analytics Engineer

Overview

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

Coinbase’s mission is to increase economic freedom in the world by building the emerging onchain platform and the future global financial system. We seek a candidate who shares this mission and is enthusiastic about crypto and blockchain technology.

Our work culture is intense and not for everyone. If you want to build the future alongside high-caliber colleagues who expect excellence, Coinbase can be the right place for you.

In-person participation is required throughout the year. Team and company-wide offsites occur 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. As an Analytics Engineer on our team, you will enable Analytics and Operations to function at scale and translate complex technical requirements into consumable front-end data solutions while influencing the overarching CX Analytics strategy.

Our team emphasizes data quality, reliability, and usability to empower stakeholders with data-driven decisions.

• Trusted Data Sources:
  Develop and maintain foundational data models serving 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 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 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 by building tables or solving problems and delivering 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: (1) Build first data pipelines and insights for a new line of business; (2) Fix data gaps for downstream data users; (3) Take initiative and accountability for issues across the stack.

• Generate business value:
  Interface with stakeholders on data and product teams to deliver commercial value from data.
• Examples:
  Build a new data model enabling downstream teams to unlock value; combine engineering details with stats and data expertise to improve algorithms; collaborate with PMs to integrate product data into a holistic framework.

• Focus on outcomes, not tools:
  Use frameworks to identify best-fit tools to deliver value.
• Examples:
  Develop abstractions to support scalable data workflows; establish internal data apps frameworks; leverage established tools (e.g., SQL) to deliver impact quickly.

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

• Customer Support Data
  
  Experience:
  
  Familiarity with data elements and processes supporting Customer Support initiatives, including performance monitoring, staffing inputs, and handling of sensitive PII across stakeholders.
• 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 (e.g., GPT) including creating, refining, and optimizing prompts for internal tools.
• Advanced SQL:
  Proficiency in advanced SQL for data transformation, querying, and optimization.
• Intermediate to Advanced Python:
  Scripting and automation, with OOP and scalable frameworks.
• Collaboration and Communication:
  Ability to translate technical concepts into business value and manage cross-functional projects.
• Data Pipeline Development:
  Experience building, maintaining, and optimizing ETL/ELT…