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.

Platform Administration

Design, deploy, and manage Databricks workspaces across Development, Test, UAT, and Production environments.

Establish workspace standards, networking, and environment isolation.

Manage platform upgrades, including Databricks Runtime (DBR) version planning, testing, and rollout.

Maintain platform availability, reliability, and performance.

Compute & Cluster Management

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.

Security & Identity Management

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.

Governance & Data Access

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.

DevOps & CI/CD

Configure Git integration with Databricks Repos.

Support CI/CD pipelines for notebooks, workflows, and infrastructure.

Collaborate with DevOps teams on automated deployments.

Enable Infrastructure as Code (IaC) using Terraform where applicable.

Monitoring & Operations

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.

Cost Optimization

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.

Compliance & Audit

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

Job clusters

Databricks Runtime management

Workflows

Databricks Repos

Unity Catalog

Secret Scopes

Service Principals

Personal Access Tokens (PATs)

AWS

IAM

VPC fundamentals

EC2 concepts

S3

CloudWatch

KMS

Security Groups

Networking fundamentals

DevOps

Git

CI/CD

Terraform

Infrastructure as Code

Azure DevOps, GitHub Actions, or Jenkins

Monitoring

Platform monitoring

Incident management

Log analysis

Performance tuning

Cost optimization

Required Experience

Candidates should have hands-on experience with:

Managing enterprise Databricks platforms

Setting up multiple Databricks workspaces

Configuring Unity Catalog

AWS IAM integration

Cluster policy creation and governance

Runtime version upgrades

CI/CD implementation

Git integration

Platform monitoring and troubleshooting

Secrets management

Audit logging

Production support

Cost optimization initiatives

Preferred Qualifications

Experience managing large-scale Databricks environments (100+ users).

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 FinOps 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 (PrivateLink, VPC endpoints).

Experience with monitoring tools such as CloudWatch, Datadog, or Splunk.

Exposure to Apache Spark internals.

Experience supporting AI/ML workloads on Databricks.

Education

Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.

Certifications (Preferred)

Databricks Certified Data Engineer Professional or Databricks Certified Platform Administrator (if available).

AWS Certified Solutions Architect – Associate/Professional.

AWS Certified SysOps Administrator.

Terraform Associate certification.

Ideal Candidate Profile