Lead Data Engineer

Nuna•San Francisco, CA

10d•$208,000 - $260,000

About The Position

At Nuna, our mission is to make high-quality healthcare affordable for everyone. We are dedicated to tackling one of our nation’s biggest problems with ingenuity, creativity, and a keen moral compass. Nuna is committed to simple principles: a rigorous understanding of data, modern technology, and most importantly, compassion and care for our fellow human. We want to know what really works, what doesn't—and why. Nuna has established its brand in the B2B space over the last decade by shifting the US healthcare system towards an incentive model that rewards healthcare providers for positive outcomes. Marshalling our collective backgrounds and insights, we are now crafting an innovative, consumer app - a clinically driven healthcare companion experience that leverages AI, gamification and social support techniques to improve outcomes for people with chronic conditions. As a sign of the impact Nuna has already made in this space, Nuna was recently selected to join the Centers for Medicare & Medicaid Services (CMS) Health Tech Ecosystem, a landmark public-private initiative designed to transform healthcare for Americans. YOUR TEAM The Data org at Nuna is an interdisciplinary group spanning data science, machine learning, data analytics, actuarial science, and research. The Data Engineer team is a core part of the broader Data organization, which is an interdisciplinary group spanning data science, machine learning, data analytics, actuarial science, and research. The Data Engineer team acts as the technical backbone for data architecture, platform development, and data operations, empowering the organization to deliver impactful data-driven solutions in healthcare. YOUR OPPORTUNITIES We are looking for someone who is excited to use their creativity and engineering skills to make a difference in healthcare. You will have a foundational role on a team building a consumer product that incentivizes healthy behavior. You will be responsible for the data architecture and direction of the data platform that powers our data operations and data science initiatives.

Requirements

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

Nice To Haves

MS in quantitative field (e.g. Data Science, Economics, Statistics, Engineering)
Experience building a data platform from zero to one
Experience working with healthcare data
Experience with SDLC and management of machine learning models (MLOps)
Bonus points if experience with MLOps on LLM/GenAi features (evals, context building, …)

Responsibilities

Own the architecture and evolution of the data platform, based on business needs and considering trade-offs in timelines, cost, and resources
Define and enforce standards for code development, contribution, and deployment for data engineering workflows.
Oversee integrations with external services, including data ingestion, distribution, and service-to-service data flows.
Contribute hands-on to data transformations and optimizations
Establish security, governance, and operational best practices for the data platform in collaboration with security and enterprise data engineering teams.
Curate and develop datasets needed to support Data org project deliverables
Collaborate with cross-functional partners in engineering, design, and product to develop solutions
Generate and prioritize new opportunities for improvements
Provide build vs buy assessments and recommendations as the platform expands