Data Engineer (Databricks)

Required Technical Skills — Databricks

• Unity Catalog — catalogue/schema design, access control, and data lineage
• Lakeflow / Delta Live Tables for pipeline orchestration; Delta Lake table format
• Databricks SQL and cluster/workspace administration (compute policies, pools, cost management)
• Databricks Asset Bundles (DABs) and Databricks Repos for CI/CD
• PySpark / Spark SQL for large-scale data transformation
• Working knowledge of Databricks system tables (audit logs, billing/usage, query history) for observability
• Minimum 5 years of hands-on experience in Data Engineering, Data Platform Engineering, or related disciplines.
• Minimum 3 years of hands-on experience with Databricks involving data pipeline development, platform administration, governance, and optimization.

Required Technical Skills — Common

• Strong SQL and Python (PySpark or general-purpose) for data engineering
• Data modeling — dimensional design, star/snowflake schemas, semantic layers
• CI/CD pipelines (e.g., Azure DevOps, GitHub Actions, GitLab CI) for data engineering workflows
• Infrastructure-as-code (Terraform preferred) for provisioning cloud data platform resources
• Hands-on experience on at least one hyperscaler — AWS, Azure, or Google Cloud

Understanding of data security and compliance frameworks applicable to government/public-sector environments

Skills

Spark SQL, Microsoft Azure, PySpark, Terraform, Data Pipeline, Azure DevOps, Databricks, Data Engineering, Cloud Infrastructure, SQL, CI/CD, Semantic Layer