Databricks Platform Engineer

Experience

5–10 years of overall IT experience, including 3–5+ years of hands‑on experience administering and managing Databricks platforms in production environments on AWS.

Job Summary

We are looking for a Databricks Platform Manager/Engineer responsible for designing, administering, securing, and optimizing the enterprise Databricks platform on AWS. The ideal candidate will own the platform lifecycle, ensuring scalability, security, governance, cost optimization, and operational excellence while enabling data engineering, analytics, and AI teams to work efficiently.

This role focuses on platform engineering rather than data engineering or analytics development.

Key Responsibilities

• Design, deploy, and manage Databricks work spaces across Development, Test, UAT, and Production environments.
• Establish workspace standards, networking, and environment isolation.
• Manage platform upgrades, including Databricks Runtime (D ) version planning, testing, and rollout.
• Maintain platform availability, reliability, and performance.
• Create and manage cluster policies to enforce organizational standards.
• Configure instance pools and job clusters.
• Prevent oversized or non‑compliant clusters.
• Standardize cluster configurations across teams.
• Optimize cluster startup times and resource utilization.

• Integrate Databricks with AWS IAM.
• Configure and manage authentication and authorization.
• Manage service principals, personal access tokens (PATs), and secret scopes.
• Implement least‑privilege access controls.
• Support enterprise identity management and SSO.

• Administer Unity Catalog.
• Manage catalogs, schemas, tables, and permissions.
• Implement role‑based access control (RBAC).
• Enforce data governance and access policies.
• Support audit and compliance requirements.

• Configure Git integration with Databricks Repos.
• Support CI/CD pipelines for notebooks, workflows, and infrastructure.
• Collaborate with Dev Ops teams on automated deployments.
• Enable Infrastructure as Code (IaC) using Terraform where applicable.

• Monitor workspace health, jobs, clusters, and platform performance.
• Respond to incidents and perform root cause analysis.
• Maintain operational dashboards and alerts.
• Support production releases and platform maintenance activities.

• Monitor Databricks usage and cloud spending.
• Recommend cluster sizing and auto‑scaling strategies.
• Optimize job scheduling and compute utilization.
• Track cost trends and recommend savings opportunities.

• Configure audit logging.
• Ensure platform complies with enterprise security policies.
• Support internal and external audits.
• Maintain operational documentation and platform standards.

Required Skills

• Databricks Platform
• Workspace administration
• Cluster management
• Cluster policies
• Instance pools
• Databricks Runtime management
• Workflows
• Databricks Repos
• Unity Catalog
• Personal Access Tokens (PATs)
• AWS
• IAM
• EC2 concepts
• S3
• Cloud Watch
• KMS
• Security Groups
• Dev Ops
• Git
• CI/CD
• Terraform
• Infrastructure as Code
• Azure Dev Ops, Git Hub Actions, or Jenkins
• Monitoring
• Platform monitoring
• Performance tuning
• Required Experience

Candidates should have hands‑on experience with:

• Managing enterprise Databricks platforms
• Setting up multiple Databricks work spaces
• Configuring Unity Catalog
• AWS IAM integration
• Cluster policy creation and governance
• CI/CD implementation
• Git integration
• Platform monitoring and troubleshooting
• Secrets management
• Audit logging
• Production support

Preferred Qualifications

• Experience supporting multiple business units or enterprise data platforms.
• Hands‑on experience with Terraform for Databricks infrastructure provisioning.
• Knowledge of Lakehouse architecture and Delta Lake.
• Experience with Fin Ops practices for cloud cost optimization.
• Familiarity with data governance and security frameworks.

Nice to Have

• Experience with AWS Organizations and Control Tower.
• Knowledge of networking concepts for private connectivity (Private Link, VPC endpoints).
• Experience with monitoring tools such as Cloud Watch, Datadog, or Splunk.
• Exposure to Apache Spark internals.
• Experience supporting AI/ML workloads on Databricks.
• Education
• Bachel…