Data Engineer (Health Data Metrics)

About Keebler Health

Keebler Health is building the operating system for value-based care. Our mission is to help risk-bearing healthcare organizations thrive in value-based arrangements by unlocking the full power of their data. We empower leading primary care groups, ACOs, and health plans to act on real-time insights that improve outcomes, reduce costs, and fuel sustainable growth.

We're a fast-moving, high-performing team, and we’re looking for people who share our bias toward speed, urgency, and excellence. We are seeking a Data Engineer to lead our efforts around reporting scalable metrics for healthcare data and AI driven suggestions.

About the role

We are seeking a skilled and motivated mid level to senior level Data Engineer to join our team and play a critical role in building and optimizing the data infrastructure that powers our healthcare AI solutions. The ideal candidate will bring expertise in modern data engineering tools and techniques, with a specific focus on healthcare quality metrics, population health, and data interoperability standards such as FHIR. Level and salary will commensurate with experience.

Key Responsibilities

Data Engineering and Development

- Design, build, and maintain scalable, efficient data pipelines for ETL/EL T processes on AWS.

- Develop, test, and deploy robust solutions using SQL and Python for data transformation and analysis.

- Implement and manage data warehousing solutions using Redshift Serverless and other AWS data services.

- Leverage dbt (Data Build Tool) for data modeling, transformation, and documentation.

- Utilize workflow orchestration tools such as Temporal for pipeline automation.

Healthcare Data Expertise

- Work with healthcare quality metrics such for value-based care and ensure data alignment with industry standards.

- Collaborate with stakeholders to integrate population health tools and analytics into data workflows.

- Develop and maintain familiarity with FHIR data models and healthcare interoperability standards for seamless integration of healthcare data sources.

- Ensure compliance with HIPAA and other healthcare regulatory requirements in all data handling processes.

Optimization and Innovation

- Identify and resolve performance bottlenecks in data pipelines, ensuring high availability and reliability.

- Optimize data storage and querying performance within Redshift Serverless and AWS infrastructure.

- Stay current with emerging trends in data engineering and healthcare technology, incorporating innovations into the data ecosystem.

Collaboration and Impact

- Partner with data and engineering teams to ensure data is accessible and meets business requirements.

- Develop scalable solutions for integrating complex healthcare datasets, ensuring data quality and accuracy.

- Contribute to the design and implementation of secure, scalable, and efficient data architecture on AWS.

Required Qualifications

- Must be US based. No foreign applicants will be considered.

- Proven experience in data engineering roles with expertise in SQL, Python, and AWS cloud-based data infrastructure.

- Experience with tools like dbt and Spark/PySpark for data transformation and modeling.

- Familiarity with healthcare data systems, including HEDIS metrics, population health tools, and FHIR data models.

- Knowledge of data warehousing and workflow orchestration tools.

Preferred Skills

- Strong understanding of healthcare data standards, including FHIR, HL7 and CQL.

- Hands-on experience with data modeling, normalization, and schema design for complex datasets.

- Experience designing and building scalable, production-grade data pipelines using orchestration tools such as Airflow, Dagster, or Temporal.

- Hands-on experience with AWS data and compute services, including Glue, EMR, Iceberg, Redshift Serverless, S3, Lambda, and related technologies.

- Strong experience with data transformation and distributed processing, using tools such as dbt, Spar