Senior Data Engineer

• Design, build, and maintain scalable, reliable data pipelines
• Develop high-performance ETL/ELT workflows using SQL, Python, PySpark, and Apache Spark
• Work with complex healthcare datasets, including clinical and claims data
• Build reliable, maintainable, and scalable ingestion and transformation workflows
• Develop and improve data quality, monitoring, alerting, and observability solutions
• Troubleshoot and resolve production data pipeline issues using Linux and shell-based tools
• Optimize data processing performance and cloud infrastructure costs
• Apply engineering practices around code quality, testing, version control, and CI/CD
• Contribute to data modeling, data warehousing, and distributed data architecture decisions
• Collaborate with Data Science, Clinical Informatics, Product, Infrastructure, and Commercial teams to translate requirements into data solutions
• Support customer onboarding and complex data integration initiatives
• Participate in design and code reviews and improve engineering practices
• Identify opportunities to improve the scalability, reliability, and efficiency of the data platform

• 5+ years of professional Data Engineering experience
• Strong SQL skills and experience working with large datasets
• Strong hands-on experience with Python and Py Spark
• Experience with Apache Spark and distributed data processing
• Hands-on experience with modern data platforms such as Databricks, Snowflake, Big Query, Redshift, or similar technologies
• Experience designing and operating production-grade ETL/ELT pipelines
• Strong understanding of data modeling, data warehousing, and distributed data systems
• Solid Linux command-line experience, including bash and shell scripting
• Experience implementing data quality, monitoring, and alerting frameworks
• Experience with Git and CI/CD
• Strong problem-solving skills and ability to independently investigate and resolve complex technical issues
• Strong communication skills and ability to collaborate effectively with cross-functional teams
• Nice to have: experience with healthcare data and standards such as OMOP, FHIR, HL7, ICD, CPT, claims, EHR/EMR, or related datasets
• Nice to have: experience with AWS, GCP, or Azure
• Nice to have: experience working with large-scale distributed systems
• Nice to have: familiarity with Airflow, Dagster, Prefect, or similar workflow orchestration tools
• Nice to have: exposure to Generative AI, LLMs, or AI-enabled data applications
• Nice to have: experience working in healthcare, life sciences, health technology, or a data-intensive environment

Demonstrates expertise in designing and maintaining scalable data pipelines, with strong proficiency in SQL, Python, and PySpark. Capable of optimizing data processing performance and implementing data quality frameworks in healthcare datasets.

• Data Engineering Experience
• SQL Proficiency
• Python and PySpark Expertise
• ETL/ELT Pipeline Design
• Data Modeling and Warehousing

• SQL
• Python
• Py Spark
• Apache Spark
• ETL/ELT Workflows
• Data Modeling
• Data Warehousing
• Linux Command-Line
• Data Quality Frameworks
• CI/CD

• Problem-Solving
• Communication
• Collaboration

• Healthcare Data
• Clinical Data
• Claims Data
• EHR
• EMR
• OMOP
• FHIR
• HL7
• ICD
• CPT

• Databricks
• Snowflake
• Big Query
• Redshift
• Git
• Airflow
• Dagster
• Prefect