· Minimum 5 years of experience as a Data Engineer with strong expertise in Azure cloud services (ADLS, Synapse Analytics, Data Factory, or similar).
· In-depth knowledge of SQL Server, including T-SQL scripting, data modeling, and query optimization.
· Proven ability to start, run, manage, and complete a technical project with minimal project management oversight.
· Prior experience as a lead, supervisor, or senior individual contributor with mentorship responsibilities.
· Strong cross-functional communication skills; able to work effectively with business partners across all branches of the organization.
· Experience with CI/CD pipelines, DevOps principles, and version control systems (Git, Azure DevOps).
· Excellent root cause analysis and problem-solving skills.
· Familiarity with healthcare data standards and regulations (HIPAA, HL7, etc.) is a strong plus.
· Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent work experience).
MUST HAVES:
· Databricks SQL (Advanced): Delta Lake, merge/upsert, CDC patterns.
· Python (Advanced): Data engineering-grade pipeline logic, CDC ingestion, file egress, SQL migration runners, PySpark/Spark DataFrame API.
· Databricks Spark Notebooks (Advanced): Notebook-based pipeline development, cluster configuration, Delta table operations.
· Airflow Python Development (Advanced): Production DAGs, dynamic DAGs, error handling, retries.
· Airflow Astro Configuration (Mid): Astronomer deployment, Astro CLI, connection/variable management.
· Azure Ecosystem (Mid): Key Vaults, Logic Apps, Astronomer on Azure, ADLS Gen2, Azure DevOps.
· T-SQL (Mid): SQL Server Change Tracking, stored procedures, source-side ETL debugging.
NICE TO HAVES:
· Azure Data Factory (Mid): Pipeline authoring, triggers, integration runtimes.
· Git + Azure DevOps CI/CD (Mid): Branching, PRs, notebook/SQL migration deployments.