Staff Analytics Engineer

Coinbase Careers Page

5d•Hybrid

About The Position

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. But 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. The CX 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. As an Analytics Engineer on our team, you will function as a force multiplier, enabling Analytics and Operations to function seamlessly at scale. You’ll have the opportunity to translate complex technical and operational requirements into easily consumable front end data solutions, while also heavily influencing the overarching strategy for CX Analytics and its partners. 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.

Requirements

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 Programming (OOP) and building scalable frameworks.
Collaboration and Communication: Strong ability to translate technical concepts into business value for cross-functional stakeholders. Proven ability to manage projects and communicate effectively across teams.
Data Pipeline Development: Experience building, maintaining, and optimizing ETL/ELT pipelines, using modern tools like dbt, Airflow, or similar.
Data Visualization: Proficiency in building polished dashboards using tools like Looker, Tableau, Superset, or Python visualization libraries (Matplotlib, Plotly).
Development Tools: Familiarity with version control (GitHub), CI/CD, and modern development workflows.
Data Architecture: Knowledge of modern data lake/warehouse architectures (e.g., Snowflake, Databricks) and transformation frameworks.
Business Acumen: Ability to understand and address business challenges through analytics engineering.
Data savvy: Familiarity with statistics and probability.

Nice To Haves

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

Responsibilities

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.
Generate business value: Interface with stakeholders on data and product teams to deliver the most commercial value from data (directly or indirectly).
Focus on outcomes not tools: Use a variety of frameworks and paradigms to identify the best-fit tools to deliver value.