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Lead Data Engineer

San Francisco, CA

At Nuna, our mission is to make high-quality healthcare affordable and accessible 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. 

YOUR TEAM

You will join an interdisciplinary group spanning data science, machine learning, data analytics, actuarial science, and data engineering. You will be part of the larger team building Nuna’s Patient and Provider products, which reward patients for healthy behaviors as they manage chronic disease.   

The team is responsible for evaluating impact and developing critical algorithmic and quantitative features across these products. This involves bringing analytical rigor to product definition, monitoring, and evaluation; design, development, and evaluation of predictive algorithms; and the maintenance and expansion of a flexible Data Platform. The team takes an ownership attitude and has a strong culture of internal review and collaboration. We work closely with engineering, product, design, and account management teams.  

YOUR OPPORTUNITIES

We are looking for someone who is excited to use their expertise in building scalable data engineering solutions to make a difference in healthcare. You will have the opportunity to play a foundational role in the success of a burgeoning product at Nuna and the ability to shape the data engineering function. The Data Platform supports multiple data products across analytics, experimentation, model serving, and more. 

  • Contribute to and expand upon the architecture of a growing Data Platform
  • Establish data engineering and software best practices in the team
  • Hands-on development of Data Platform capabilities - e.g. write code to ingest and transform complex data sources, maintain deployment process, etc.
  • Articulate pros and cons of competing tools to inform choices and build vs buy decisions
  • Serve as a mentor for team members in code quality and review
  • Work closely with data scientists, analysts, and actuaries to identify opportunities to improve workflows and increase overall efficiency

QUALIFICATIONS

Required Qualifications

  • BS/MS in a quantitative field (e.g. Computer Science, Engineering) + 10 years of engineering/equivalent experience 
  • 7+ years of experience building systems that manage the ingest, transformation, and management of multimodal data types
  • Deep knowledge of modern data infrastructure best practices   
  • Proficiency with tools spanning the range of the following: AWS S3, Terraform, Airflow, Glue / Unity catalog, pytest, spark/pyspark, python, SQL (or analogous)
  • Experience with data governance, traceability, etc.
  • Proficiency with cloud platforms (AWS preferred) 
  • Experience in a zero-to-one setting
  • Interest in improving healthcare and working with interdisciplinary project teams
  • Clear communication and presentation skills 

Preferred Qualifications

  • Experience with Databricks product suite, especially Unity catalog
  • Experience in a HIPAA-compliant environment
  • MLOps experience 

We take into account an individual’s qualifications, skillset, and experience in determining final salary. This role is eligible for health insurance, life insurance, retirement benefits, participation in the company’s equity program, paid time off, including vacation and sick leave. The expected salary range for this position is $188,000 to $230,750. The actual offer will be at the company’s sole discretion and determined by relevant business considerations, including the final candidate’s qualifications, years of experience, and skillset.

#LI-FK1 #LI-Hybrid

Nuna is an Equal Employment Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, genetics and/or veteran status.

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