Sentara is hiring a Data Engineer!
This is a fully remote position.
Overview
We are looking for a Data Engineer to support a modern data platform built on Databricks. This role will focus on building scalable data pipelines, supporting a metadata-driven ingestion framework, and ensuring data quality and governance are embedded into the platform.
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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