Director Of Data Engineering

About the Role

As a Director Data Engineer, you will be a hands-on technical expert responsible for designing, building, and optimizing the core data platform that transforms complex healthcare data into actionable insights. You will write high-quality, production-grade code to develop robust data pipelines, implement best-in-class data quality checks, and drive the technical evolution of our data architecture. This role is a senior individual contributor position, focused on solving the most challenging technical problems in clinical decision support and research and ensuring the integrity and performance of our large-scale clinical datasets.

Responsibilities

- Pipeline Development
: Design, build, and maintain scalable and reliable data pipelines (ETL/ELT) for the ingestion and processing of large volumes of customer clinical data.
- Data Transformation
: Directly implement code to efficiently map diverse customer datasets into our common data models (e.g., OMOP, FHIR), ensuring data fidelity and consistency.
- Architecture & Optimization
: Identify and implement technical improvements to the data engineering architecture, including optimizing distributed data processing and cloud resource utilization for cost and performance.
- Quality & Governance
: Develop and embed advanced data quality checks, monitoring, and validation frameworks to maintain the highest standards of data reliability in clinical datasets.
- Technical Collaboration
: Partner with Software Engineering and Data Science teams to translate complex business requirements into robust, scalable technical solutions and data models.
- Mentorship & Standards
: Act as a technical leader, establishing coding standards, performing code reviews, and mentoring mid-level engineers on deep technical subjects.

Minimum Qualifications

• Bachelor's degree in Computer Science, Data Engineering, or a related field; Master’s degree preferred.
• 8+ years of hands-on experience in data engineering, focused on large-scale data systems.
• Expert-level proficiency in SQL (any dialect) and Python, with deep experience in cloud platforms such as AWS, Azure, or GCP.
• Extensive, proven track record of working hands-on with healthcare data, including advanced knowledge of relevant standards and data models (e.g., FHIR, OMOP).
• Deep technical mastery of distributed data processing and streaming frameworks like PySpark, and experience with workflow orchestration tools (e.g., Airflow, Dagster).