Databricks Data Engineer (Spark AND Unity)

Interview - Face to face

Position Overview:

Build and maintain scalable data pipelines using Azure Databricks to support DATA & AI ACDP projects. Apply 3-4 years of experience to deliver reliable ETL processes and collaborate on data-driven insights.

Key Responsibilities:

• Design, build, and optimize data pipelines in Azure Databricks for ingestion, ETL/ELT, and transformations, implementing Medallion Architecture (Bronze, Silver, Gold) with Delta Lake for data quality and versioning
• Utilize Databricks Spark (PySpark, SQL), Delta Live Tables, and Unity Catalog for pipeline development, governance, and basic streaming
• Integrate data from ADLS Gen2, Azure SQL Database, and SQL Pools; support orchestration via Databricks workflows
• Implement CI/CD in Azure DevOps for Databricks deployments, including Bicep templates and ADF integration
• Apply data quality measures (expectations, constraints), security (RBAC, encryption), and monitoring for performance/cost efficiency using auto-scaling and Photon
• Work with analysts and stakeholders to refine requirements into functional workflows; handle data cleansing, modeling, and Python/SQL scripting for structured/unstructured data
• Troubleshoot pipelines and contribute to documentation/best practices

Required Qualifications:

• 3-4 years in data engineering, focused on Azure Databricks pipelines and ETL
• Proficiency in PySpark, SQL, Python; experience with cloud storage and basic orchestration
• Familiarity with Azure fundamentals and collaborative tools