Senior Data Engineer

Function Health•Canada, KS

About The Position

As a Data Engineer, you will design, build, and scale the core data infrastructure that powers Function Health’s imaging, analytics, and AI ecosystems. You’ll work closely with the Data, AI, and R&D teams to orchestrate secure, reliable, and compliant pipelines across a wide range of healthcare data types. This role focuses on building robust orchestration and cloud-native data systems (Airflow, AWS, Databricks) that support high-volume, heterogeneous datasets — from DICOM imaging to biomarkers, reports, and other structured and unstructured health data. You’ll help ensure that these systems are performant, scalable, and production-ready as our platform and data footprint continue to grow. You will be working closely with the Data team and AI engineering team and reporting to Radhika Tibrewala.

Requirements

3+ years of experience in data engineering, ETL development, or cloud-based data orchestration.
Strong proficiency in Python and SQL. 3+ years of experience in data science, analytics, or applied research, IF hold a PHD/published research
Hands-on experience with workflow orchestration tools (Airflow, Prefect, or similar).
Experience with cloud platforms and services (AWS, Azure, GCP familiarity is a plus).
Experience building on Databricks and working with distributed processing frameworks (Apache Spark, Dask, or similar).
Solid understanding of data validation, observability, testing, and production best practices.
Strong communication and documentation skills; comfortable working across technical teams.

Nice To Haves

Experience with healthcare, imaging, or PHI-sensitive data is a strong plus.

Responsibilities

Design and operate scalable data pipelines and orchestration systems supporting imaging and biomarker data (e.g., Airflow, cloud services, distributed compute platforms).
Contribute to PHI-safe, compliant data infrastructure aligned with healthcare standards (HIPAA, GDPR, etc.).
Enhance performance and fault-tolerance across distributed data workflows.
Partner with the data management lead to integrate DICOM and data pipelines into the broader data lake and analytics stack.
Implement robust data validation, observability, and monitoring systems for production pipelines.
Collaborate with data science, ML, and product teams to ensure data reliability and accessibility.
Support automated ingestion, transformation, cataloging, and access patterns for downstream analytics and ML use cases.