Senior Analytics Engineer, Color

• 5+ years of experience using data to drive product growth and operational efficiency
• 5+ years of experience as an analytics engineer or in a similar role.
• Advanced proficiency in SQL, data model design, and data warehouse platforms.
• Working knowledge of Python and data science tools
• Deep interest in the modern data stack
• Excellent communication skills and ability to work with technical and non-technical partners from many teams, especially in exploring decisions and trade-offs
• Experience driving complex projects from design to completion
• Commitment to software engineering best practices and applying them in an analytics setting
• A strong desire to work at the intersection of healthcare and technology, driven by the opportunity to make preventive care more accessible and equitable
• A strong desire to adapt to new emerging technology and AI tools to scale the output of the team
• This is a role for someone who thrives at the intersection of technical engineering and cross-functional strategy — eager to shape both the infrastructure and the culture that enable data-driven decisions at scale, while advancing Color’s mission to transform cancer care
• Apply to join Color and do the most meaningful work of your career. If you are not sure that you’re 100% qualified but are up for the challenge - we want you to apply!

Desirable

• As a Senior Analytics Engineer at Color, you will be the bridge between stakeholders and scalable data solutions
• You’ll create and maintain the foundational data models that power insights into our cancer screening programs — enabling us to demonstrate impact, identify areas for improvement, and drive product and business decisions with confidence
• Partnering across Data, Product, and Engineering, you’ll help define and maintain trusted technical definitions that support experimentation and ensure accurate measurement of program success
• Beyond delivering dashboards and metrics, you’ll look for patterns in stakeholder needs across BI, product analytics, and client analytics, designing durable systems that go beyond ad hoc solutions: building trusted data infrastructure, self-service tools, and education that empower teams to answer their own questions
• You’ll also experiment with AI and automation to scale analytics workflows and unlock new capabilities
• Build foundational data models that help Color demonstrate program value to employers and prospective clients
• Translate business needs and source data into robust, scalable models that power accurate, insightful analysis
• Collaborate across Product, Engineering, Operations, GTM, Clinical, and Client teams — clarifying ambiguity and balancing scope vs. impact to deliver meaningful solutions
• Define and maintain business logic in dbt, keeping the warehouse aligned with evolving products, data, and business needs
• Create self-service data layers that speak the language of business users, enabling intuitive, independent exploration
• Partner with BI and analytics teams to align on metrics and ensure consistency and trust across dashboards
• Identify recurring patterns and build reusable solutions that reduce ad hoc work and improve scalability
• Use emerging AI and automation tools to streamline analytics workflows and boost team efficiency
• Contribute to a culture of data-driven decision-making by mentoring peers and sharing best practices in analytics engineering
• Possible Projects to Contribute to:
• Expand self-service data models in collaboration with analysts to drive adoption and empower teams across Color
• Today, more than half the company uses dashboards we’ve built, and over one-third of employees create their own analyses thanks to our robust self-service models — and we’re eager to grow this impact even further
• Develop and enhance customer-facing data models that power the next generation of dashboards, giving clients visibility into program health (e.g., engagement rates, participation trends) and population outcomes (e.g., areas of vulnerability where Color can provide support)
• Build a conformed medical claims data model that unifies disparate sources, enabling consistent and accurate analysis of healthcare utilization and outcomes
• Optimize data infrastructure performance and costs, ensuring our pipelines and warehouses are efficient, scalable, and reliable as our data volume and complexity grow