Hybrid: Databricks Engineer

Requirements

• Strong expertise in Databricks (Delta Lake, Unity Catalog, Lakehouse Architecture, Table Triggers, Delta Live Pipelines, Databricks Runtime etc.)
• Proficiency in Azure Cloud Services.
• Solid Understanding of Spark and PySpark for big data processing.
• Experience in relational databases.
• Knowledge on Databricks Asset Bundles and GitLab.

Responsibilities:

Data Pipeline Development:

• Build and maintain scalable ETL/ELT pipelines using Databricks.
• Leverage PySpark/Spark and SQL to transform and process large datasets.
• Integrate data from multiple sources including Azure Blob Storage, ADLS and other relational/non-relational systems.

Collaboration & Analysis:

• Work Closely with multiple teams to prepare data for dashboard and BI Tools.
• Collaborate with cross-functional teams to understand business requirements and deliver tailored data solutions.

Performance & Optimization:

• Optimize Databricks workloads for cost efficiency and performance.
• Monitor and troubleshoot data pipelines to ensure reliability and accuracy.

Governance & Security:

• Implement and manage data security, access controls and governance standards using Unity Catalog.
• Ensure compliance with organizational and regulatory data policies.

Deployment:

• Leverage Databricks Asset Bundles for seamless deployment of Databricks jobs, notebooks and configurations across environments.
• Manage version control for Databricks artifacts and collaborate with team to maintain development best practices.