Senior Analytics Engineer

Details

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. 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.

The Analytics Engineering team bridges the gap between 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 across the organization to make data-driven decisions.

Our team combines technical expertise with a deep understanding of the business to unlock the full potential of our data. We prioritize data quality, reliability, and usability, ensuring stakeholders can rely on our data to drive meaningful outcomes.

Responsibilities

• Act as a hybrid Data Engineer/Data Scientist/Business Analyst: understand data flows end-to-end and apply engineering to extract value (building tables/pipelines) and deliver insights.
• Quickly build subject matter expertise in a specific business area and data domain; understand data flows from creation, ingestion, transformation, and delivery.
  • Examples: deliver first data pipelines and insights for a new line of business; communicate with engineering teams to fix data gaps; take accountability for fixing issues anywhere in the stack.
• Interface with stakeholders on data and product teams to deliver commercial value from data (directly or indirectly).
  • Examples: build new data models for downstream DS teams; combine engineering details with stats and data expertise to improve algorithms; work with PMs to tie product data into holistic frameworks to optimize business metrics.
• Focus on outcomes over specific tools: choose best-fit tools and frameworks to deliver value quickly while ensuring long-term maintainability.
  • Examples: develop abstractions (UDFs, Python packages, dashboards) to support scalable data workflows; stand up frameworks for internal data apps; use established tools with mastery (e.g., Google Sheets, SQL) when speed is a priority.

Requirements

• Data modeling expertise: strong understanding of best practices for designing modular and reusable data models (star/snowflake schemas).
• Prompt design and engineering: expertise in prompt engineering and design for LLMs (e.g., GPT) including creating, refining, and optimizing prompts for internal tools and use cases.
• Advanced SQL: proficiency in advanced SQL techniques for transformation, querying, and optimization.
• Intermediate to advanced Python: scripting, automation, OOP, and building scalable frameworks.
• Collaboration and communication: translate technical concepts into business value; manage projects and communicate effectively across teams.
• Data pipeline development: build, maintain, and optimize ETL/ELT pipelines using modern tools like dbt, Airflow, or similar.
• Data visualization: build polished dashboards with Looker, Tableau, Superset, or Python visualization libraries (Matplotlib, Plotly).
• Development tools: familiarity with GitHub, CI/CD, and modern development workflows.
• Data architecture: knowledge of modern data lake/warehouse architectures (Snowflake, Databricks) and transformation frameworks.
• Business acumen: understand and address business challenges through analytics engineering.
• Statistics/probability: familiarity with statistics and probability.

Bonus skills:

• Experience with cloud platforms (AWS, GCP).
• Familiarity with Docker or Kubernetes.

Benefits

• Target annual salary range (full-time): $191,000—$191,000 CAD. Full-time offers also include bonus eligibility, equity eligibility, and benefits (medical, dental, vision).
• Extended Health Care Benefit (coordinates with provincial coverage), dental, vision, Consult+ (virtual health), life & accident insurance, disability coverage.
• Employee Stock Purchase Plan (ESPP), wellness stipend, mobile/internet reimbursement, connections stipend, learning and development allowance, employee assistance program, travel medical policy, fertility benefits, generous time off/leave policy.

Additional Notes

• Applying does not guarantee consideration for the exact role; leveling and team matching are assessed during the interview process.
• Coinbase is an Equal Opportunity Employer and will provide accommodations for applicants with disabilities.
• ID: G2754