Lead Data Engineer, Nuna Incorporated

• Deep hands-on expertise in designing, coding, developing, and maintaining data platforms that support data analytics and data science use cases
• Proven ability to design, develop and implement robust data ingestion pipelines (ETL) from external sources into a data platform
• Experience establishing standards for code development, deployment, and contributions in a data engineering environment
• Ability to solicit and translate customer and business needs into requirements and an evaluation framework
• Interest in improving healthcare and working with interdisciplinary project teams
• Clear communication and presentation skills
• Experience with Databricks
• Expertise in data platform languages such as python, pyspark and SQL
• + 5-10 years of industry experience with technical lead experience of running a data platform for business operations

Desirable

• This is a foundational, hands-on role for someone who wants to build a data platform from zero to one and shape its direction as Nuna grows. You'll own the architecture and make the calls on standards, build-vs-buy, and trade-offs — often with incomplete information and the freedom to define the right answer yourself. Comfort with ambiguity isn't a nice-to-have; it is the job
• Own the architecture and evolution of the data platform, weighing trade-offs across timelines, cost, and resources
• Build and optimize the transformations, pipelines, and datasets that power our analytics and data science work
• Design and maintain integrations with external services
• Define and enforce standards for how data engineering code gets developed, reviewed, and deployed
• Establish security, governance, and operational best practices, in partnership with our security and enterprise data engineering teams
• Provide build-vs-buy assessments as the platform expands, and surface new opportunities to improve it
• Partner with engineering, design, product, and the wider Data org to turn business needs into working solutions
• You'll work shoulder to shoulder with both scientists and engineers, on a team led by a data scientist. That means the platform you build isn't an abstraction: it serves real modeling, research, and analytics needs, and you'll see exactly who depends on it and why