Lead Data Platform Engineer

We are expanding our Platform Engineering capability to build, secure, and automate the enterprise Data Platform on cloud and Databricks. This role owns the underlying infrastructure, ingestion frameworks, CI/CD pipelines, orchestration, and observability that enable Data Engineers and Analytics teams to operate at scale. The ideal candidate combines strong platform engineering fundamentals with hands-on DevOps skills across data replication, job scheduling, deployment automation, and cloud operations.

Infrastructure & Platform Engineering:

• Deploy and maintain Databricks workspaces and cloud infrastructure using Infrastructure-as-Code.
• Manage platform upgrades, patching, new flow setup, and environment refresh support.
• Support enterprise data replication (HVR) and file-based ingestion patterns from operational systems into the data platform.

Orchestration & Job Scheduling:

• Provide monitoring, recovery, and operational support for enterprise job scheduling and orchestration.
• Configure job dependencies and coordinate with source teams on long-running workloads.

CI/CD & Deployment Automation:

• Design and maintain GitLab CI/CD pipelines for data and platform projects with automated deployment workflows.
• Standardize deployment strategies using reusable templates and Databricks-native deployment tooling.
• Implement branching strategies, code review policies, and environment promotion rules.
• Support the Change Request (CR) deployment lifecycle, including validation and ticket closure.

Monitoring, Reliability & Support:

• Configure monitoring, alerting, and logging to ensure platform stability.
• Serve as an escalation point for platform-related incidents and vendor coordination.
• Support year-end activities and compliance reporting requirements.

What Success Looks Like (First 6–12 Months):

• In your first 6–12 months, you'll stabilize CI/CD and monitoring for key platform flows, automate recurring operational tasks, and streamline the change-request and deployment lifecycle.

• Bachelor's or Master's degree in Computer Science, Information Technology, or equivalent relevant experience.
• 6+ years of industry experience in Data Engineering, Cloud Infrastructure, or DevOps.
• Hands-on experience with CI/CD tooling (GitLab preferred) — pipeline authoring, release management, and secrets management.
• Strong grounding in cloud platforms (AWS preferred) for data workloads.
• Working knowledge of Databricks platform administration.
• Experience with monitoring and observability tools, proactive alerting, and incident triage.
• Proficient in Python and Bash/Shell scripting for automation.

• Experience with enterprise data replication tools (e.g. HVR).
• Advanced Infrastructure-as-Code skills.
• Familiarity with enterprise job orchestration platforms (e.g. Autopilot).
• Exposure to Databricks Serverless Compute and Workflow orchestration.
• Cloud Solutions Architect or Databricks certifications are a plus.

• Reliability-first mindset — focus on stability, automation, and self-healing systems.
• Strong sense of ownership across the platform lifecycle — build, run, and evolve.
• Effective vendor coordination and cross-team collaboration.
• Clear documentation and knowledge-sharing habits.

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