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