Databricks Architect — GCP Focus

Databricks Architect — GCP Focus
Location: Remote
Duration: 12 Months

Purpose:
Design, build, and evolve Databricks on GCP as an enterprise data and analytics platform.

This is not only a Databricks product architecture role. The architect must have a strong understanding of GCP and know how Databricks integrates with the underlying cloud infrastructure.

The role will also work with our existing Databricks on AWS environment, so familiarity with AWS and the ability to understand existing AWS architecture and patterns is important.

What You Will Own
Databricks architecture on GCP
Databricks account and workspace architecture
Unity Catalog and enterprise data governance
GCP storage integration with Databricks
GCP IAM and service account integration
Databricks networking and private connectivity on GCP
Workspace and network isolation patterns
Compute architecture — Serverless, Classic, Jobs and SQL Warehouses
Catalog, schema and multi-tenant architecture
Cross-cloud data sharing between AWS and GCP
Delta Sharing and Lakehouse Federation patterns
Databricks security and access architecture
Terraform and CI/CD patterns for Databricks
Cost governance and platform guardrails
Reference architectures and reusable engineering patterns

What We Need
We are looking for someone who is strong in both Databricks and GCP.
The ideal candidate understands that successfully hosting Databricks on GCP requires knowledge beyond Spark, notebooks and data pipelines.

They should understand how Databricks interacts with:
GCP VPCs and networking
Private Service Connect
Cloud DNS
GCS
IAM and service accounts
Workload Identity
KMS/CMEK
Organization policies
Private connectivity
Security controls
Terraform and infrastructure automation

Strong Databricks experience should include:
Unity Catalog
Delta Lake
Delta Sharing
Lakehouse Federation
Serverless Compute
SQL Warehouses
Structured Streaming
Databricks Asset Bundles
Platform governance and security
Multi-workspace and multi-tenant environments
AWS familiarity is also important. The candidate should be able to understand an existing AWS Databricks environment and work with concepts such as S3, IAM, VPC, PrivateLink, KMS and Route 53.

Highly Valuable Experience:
Previous experience supporting or working closely with Data Analytics, Data Engineering, Data Science, or Enterprise Data Platform teams is highly desirable.

We want someone who understands not only how to build the platform, but also how data and analytics teams actually consume it.

What Success Looks Like:
Databricks becomes a first-class platform on GCP.
The platform is securely integrated with GCP networking, identity, storage and security services.

GCP and Databricks architecture work together.
Databricks is not treated as an isolated SaaS product; its architecture is designed as part of the broader GCP platform.

AWS and GCP can coexist.
Existing AWS patterns are understood, and appropriate cross-cloud connectivity, governance and data-sharing patterns are established.

Data teams have a paved path.
Data Engineering and Analytics teams can onboard and deploy workloads using reusable platform patterns rather than custom infrastructure.

Platform capabilities are automated.
Terraform, CI/CD and reusable templates make platform deployment and onboarding repeatable.