Senior Analytics Engineer (Platform - Financial Analytics)

About the Role

The day-to-day responsibilities of this role will involve working closely with cross-functional teams, including Product, Engineering, and Data Science, to identify data gaps, define requirements, and deliver data products that meet the needs of the business. You will own end-to-end data solutions for specific business domains, from designing modular data models to building and optimizing ETL/ELT pipelines using modern tools like dbt and Airflow.

Coinbase is a remote-first company, which means you will have the flexibility to work from anywhere in the USA. However, you can expect to participate in quarterly in-person working sessions, known as "surges," which are designed to foster collaboration and innovation among team members.

What You Will Do

• Own end-to-end data modeling for assigned business domains, from understanding source system data flows through designing modular, reusable models that serve as the single source of truth for downstream teams.
• Build and optimize ETL/ELT pipelines using modern tools like dbt and Airflow, ensuring data quality, reliability, and performance at scale across Snowflake or similar warehouse architectures.
• Partner with Engineering, Product, and Data Science teams to identify data gaps, define requirements, and deliver data products that directly enable experimentation, ad hoc analysis, and business metric optimization.
• Develop scalable abstractions and frameworks that multiply the efficiency of other data teams and reduce time-to-insight across the organization.
• Design and deliver dashboards and visualization layers using tools like Looker or Tableau, translating complex data into clear, actionable views for cross-functional stakeholders.
• Collaborate with stakeholders to understand business requirements and develop data solutions that meet their needs.
• Stay up-to-date with emerging trends and technologies in the field of analytics engineering, applying this knowledge to continuously improve the company's data infrastructure and capabilities.
• Develop and maintain documentation of data models, pipelines, and other data assets, ensuring that knowledge is shared across the organization.
• Participate in code reviews and contribute to the improvement of the company's data engineering practices.

What We Are Looking For

• 5+ years of experience in analytics engineering or data engineering, with demonstrated expertise in designing modular data models and building production ETL/ELT pipelines using dbt, Airflow, or similar tools.
• Advanced SQL proficiency for complex transformations and query optimization, plus intermediate-to-advanced Python skills for scripting, automation, and building scalable frameworks.
• Production experience with modern data warehouse architectures, including performance tuning, data quality monitoring, and version-controlled development workflows.
• Proven track record of delivering data solutions that have generated measurable business impact, with the ability to independently build domain expertise and communicate technical trade-offs to stakeholders.
• Experience with prompt engineering for LLMs, including designing and optimizing prompts for internal tooling and automation use cases.
• Strong understanding of data quality principles and practices, with the ability to design and implement data validation and testing frameworks.
• Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders.

Nice to Have

• Experience with cloud-based data platforms, such as AWS or GCP, and their associated data services.
• Knowledge of data governance and security principles, with experience implementing data access controls and ensuring compliance with regulatory requirements.
• Familiarity with agile development methodologies and version control systems, such as Git.
• Experience with data visualization tools, such as Tableau or Power BI, and the ability to create interactive and dynamic dashboards.

Benefits and Perks

• Competitive salary and equity package, with the opportunity to earn bonuses based on performance.
• Comprehensive health insurance, including medical, dental, and vision coverage.
• Flexible PTO policy, with the ability to take time off as needed to recharge and relax.
• Remote work stipend, to support the setup and maintenance of a home office.
• Access to a range of professional development opportunities, including training, mentorship, and conference sponsorships.
• The chance to work with a talented and diverse team of professionals who are passionate about driving innovation in the financial sector.
• Quarterly in-person working sessions, which provide the opportunity to connect with colleagues and collaborate on projects in person.

• Tip: Make sure to highlight your experience with modern data tools and technologies, such as dbt and Airflow, in your application materials.
• When preparing for the interview, be ready to discuss your approach to data modeling, ETL/ELT pipelines, and data quality, as well as your experience working with cross-functional teams.
• To stand out as a candidate, consider creating a portfolio of your work, including examples of data models, pipelines, and visualizations you have developed in previous roles.
• During salary negotiations, be prepared to discuss your expectations and requirements, and be open to feedback and counteroffers from the company.
• When evaluating the company and role, consider factors such as the team's dynamics, the company culture, and the opportunities for growth and professional development.
• Be aware of the company's expectations around remote work, including the need to participate in quarterly in-person working sessions, and be prepared to discuss your ability to work effectively in a remote environment.