Sentara Health
Data Engineer- Remote
Rol remoto de Data Engineering con fit claro de ubicación del candidato.
Publicado22 jun 2026
Países elegibles2 países aceptados
Señal de seniorityMiddle
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
GeorgiaEstados Unidos
Resumen del rol
Data Engineer- Remote
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
Key Responsibilities
- Develop and maintain data pipelines using PySpark and Databricks
- Work within a metadata-driven ingestion framework to onboard new datasets
- Implement data quality checks and validation rules within pipelines
- Support ingestion from file-based sources and ingestion tools (e.g., Fivetran)
- Handle schema changes, incremental loads, and file processing patterns
- Contribute to data governance practices including tagging, metadata, and lineage
- Troubleshoot and resolve pipeline failures and performance issues
- Collaborate with architects and stakeholders on data onboarding and requirements
- Follow and contribute to coding standards, reusable components, and best practices
Education
- Experience in lieu of a Bachelor’s Degree
- 3+ years of relevant experience with a degree
- 5+ years of relevant experience without a degree
Certification/Licensure
- No specific certification or licensure requirements
Experience
- Required to 3 to 5 years of relevant experience
- Hands-on experience with PySpark and Databricks
- Strong SQL skills
- Experience building ETL/ELT data pipelines
- Understanding of Delta Lake concepts (merge, schema evolution, partitions)
- Familiarity with cloud platforms (Azure preferred)
- Basic experience with Git and version control
- Exposure to data catalog or governance tools (e.g., DataHub)
- Experience with Fivetran or similar ingestion tools
- Understanding of data quality and validation concepts
- Experience working with metadata-driven frameworks
- Strong problem-solving and debugging skills
- Ability to work in a structured, framework-driven environment
- Focus on data quality, not just pipeline execution
- Willingness to learn and adapt in a fast-evolving data ecosystem
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