Senior Analytics Engineer

Unchain Data; United States; On-site; USD 180,000 - 212,000; Full time

Job summary

Coinbase is seeking a data-focused Analytics Engineer to bridge data engineering, data science, and business analytics. You will build scalable data pipelines, data models, and tools that empower stakeholders to make data-driven decisions.

You will partner with cross-functional teams to ensure data quality, reliability, and usability, enabling rapid insights and measurable business impact. This role emphasizes collaboration, engineering excellence, and end-to-end data solutions.

Qualifications

• Proven ability to design modular, reusable data models (star/snowflake).
• Expertise in prompt engineering for LLMs, with impact on internal tools and use cases.
• Proficiency in advanced SQL for data transformation, querying and optimization.
• Strong Python skills, including OOP and building scalable frameworks.
• Excellent cross-functional communication and project management capabilities.
• Experience building, maintaining and optimizing ETL/ELT pipelines with modern tools.
• Ability to build polished dashboards with Looker/Tableau/Superset or Python visuals.
• Familiarity with GitHub, CI/CD and modern development workflows.
• Knowledge of data lake/warehouse architectures (Snowflake, Databricks).
• Business acumen to translate analytics into business value.
• Solid grounding in statistics and probability.

Responsibilities

• Be the expert: quickly own a data domain, map data flows end-to-end and enable pipelines and insights.
• Generate business value: collaborate with stakeholders to maximize commercial value from data.
• Focus on outcomes: choose best-fit tools and build scalable data apps and dashboards.
• Develop abstractions: create reusable components (UDFs, Python packages, dashboards).
• Stand up a framework for internal data apps, enabling DS teams to add value quickly.
• Leverage tools to deliver impact when speed matters (SQL, spreadsheets, dashboards).

Skills

Data Modeling

Prompt Design

Advanced SQL

Python (OO/ scripting)

Collaboration & Communication

Data Pipeline Development

Data Visualization

Development Tools

Data Architecture

Business Acumen

Statistical Thinking

Tools

dbt

Airflow

Looker

Tableau

Superset

GitHub

CI/CD

Databricks

Snowflake

Job description

About Us

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.

About the Role

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.

What We Do

• Trusted Data Sources: Develop and maintain foundational data models that serve as the single source of truth for analytics across the organization.
• Actionable Insights: Empower stakeholders by translating business requirements into scalable data models, dashboards, and tools.
• Cross-Functional Collaboration: Partner with engineering, data science, product, and business teams to ensure alignment on priorities and data solutions.
• Scalable Data Products: Build frameworks, tools, and workflows that maximize efficiency for data users, while maintaining high standards of data quality and performance.
• Outcome-Focused Solutions: Use modern development and analytics tools to deliver value quickly, while ensuring long-term maintainability.

Responsibilities

Analytics engineer is a hybrid Data Engineer/Data Scientist/Business Analyst role that has the expertise to understand data flows end to end, and the engineering toolkit to extract the most value out of it indirectly (building tables) or directly (solving problems, delivering insights).

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

Requirements

In addition to out of the box thinking, attention to detail, a sense of urgency and a high degree of autonomy and accountability, we expect you to have the following skills:

• 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 Have

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

Compensation

Depending on your work location, the target annual base salary for this position can range from $180,370 to $212,200 USD. Total compensation may also include equity and bonus eligibility and benefits (including medical, dental, vision and 401(k)).