We are looking for a
hands-on Databricks & GCP Data Platform Architect
who will
design and personally implement
scalable Lakehouse solutions on Google Cloud Platform (GCP).
This role requires deep technical involvement, including
building pipelines, configuring Databricks, and troubleshooting production issues
, in addition to architecture ownership.
Key Responsibilities
1. Architecture & Hands-on Implementation
Design
end-to-end Databricks Lakehouse architecture on GCP
Hands-on implementation
of:
Databricks workspaces, clusters, jobs, and workflows
Delta Lake–based Bronze / Silver / Gold data layers
Batch and streaming pipelines using Spark and Databricks
Create reference implementations and reusable frameworks for teams
Actively participate in coding, reviews, and production deployments
2. Data Engineering (Hands-on)
Build and optimize
Spark jobs and Databricks notebooks
Implement ingestion pipelines from:
Databases and enterprise applications
Streaming sources (Pub/Sub, Kafka)
External and SaaS systems
Perform performance tuning and cost optimization
Troubleshoot pipeline failures and production issues directly
3. Security, Governance & Compliance
Implement
(not just define) governance using
Unity Catalog
Configure access control integrated with
GCP IAM
Set up secure networking (VPC, private endpoints)
Enable audit logging, lineage, and data classification
Work closely with security teams to operationalize standards
4. DevOps, Automation & Operations (Hands-on)
Build CI/CD pipelines for Databricks notebooks, jobs, and configs
Implement Infrastructure as Code using
Terraform
Set up monitoring, alerting, and operational dashboards
Participate in production support, root-cause analysis, and fixes
Drive hands-on cost optimization initiatives
5. Stakeholder Collaboration
Translate business requirements into
implemented solutions
Guide and mentor data engineers through
code-level support
Conduct architecture and code reviews
Act as a technical owner from design through production
Required Skills & Experience
Must Have
Strong
hands-on experience
with
Databricks (Apache Spark)
Proven experience
building and deploying Lakehouse architectures
Hands-on experience with
GCP
, including:
Google Cloud Storage (GCS)
BigQuery
Pub/Sub
IAM & VPC basics
Experience implementing
batch and streaming pipelines
Strong troubleshooting and production support skills
Good to Have
Unity Catalog, Delta Live Tables
CI/CD, Git, Terraform
MLflow, Vertex AI exposure
Multi-cloud Databricks experience (Azure / AWS)
8–12 years of experience in data engineering / data platforms
3+ years in a
hands-on architect or senior technical lead role
All your information will be kept confidential according to EEO guidelines.
