Databricks Data Engineer

Key Responsibilities

• Design, build, and operate end-to-end data pipelines (ingestion → transformation → serving) on Databricks and/or Snowflake, following medallion/layered architecture patterns
• Configure and administer workspaces/accounts — compute policies, resource sizing, environment setup, and access hierarchy — per the SDP reference architecture
• Implement data governance controls: catalogue and schema design, RBAC, row/column-level security, data masking, and lineage tracking
• Set up CI/CD and infrastructure-as-code for pipeline deployment and environment promotion (dev → test → prod)
• Configure monitoring, telemetry, and audit logging to meet SDP's central observability and security posture requirements
• Support UAT, integration testing, and parallel-run validation during migration and go-live
• Produce handover documentation (runbooks, access lists, escalation procedures) for agency operations teams
• Work directly with client, agency stakeholders, and Principal (Databricks/Snowflake) solution architects throughout delivery

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

• 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

Preferred Qualifications

• Databricks Certified Data Engineer Associate/Professional
• SnowPro Core, or SnowPro Advanced: Data Engineer
• Prior experience delivering on a government or regulated‑sector data platform, or exposure to compliance frameworks such as IM8 is an add on
• Experience working as part of a System Integrator (SI) delivery team alongside a platform Principal is an add on