Own Databricks workspace architecture, governance, and lifecycle management
Implement infrastructure as code using Terraform/Terramate
Build and support end‑to‑end data pipelines, from ingestion to analytics and ML
Develop and maintain CI/CD pipelines for infrastructure, data pipelines, notebooks, SQL, and ML artifacts
Operate and optimize Amazon EKS clusters supporting data workloads
Define and monitor SLIs/SLOs for platform reliability and data quality
Drive innovation and continuous improvement across platform architecture and tooling
Lead incident response, on‑call rotations, and blameless post‑mortems
Implement strong security, compliance, and governance controls
Mentor engineers and influence platform standards and best practices
Must‑Have Qualifications
System Design & Innovation
Proven experience designing complex, distributed data systems end‑to‑end
Strong system design skills across cloud infrastructure, data pipelines, storage, governance, and observability
Ability to evaluate and communicate architectural trade‑offs (scalability, reliability, cost, security, operability)
Demonstrated innovation mindset, such as
Modernizing or simplifying legacy data platforms
Introducing new architectural patterns or platform capabilities
Improving reliability, performance, or developer experience through design
Cloud & Data Platform Expertise
8+ years building and operating large‑scale cloud platforms
5+ years in Data Platform, DevOps, SRE, or DataOps roles
Expert‑level experience with AWS, including networking, IAM, security, and multi‑account environments
Strong hands‑on experience with Databricks Lakehouse Platform, including:
Unity Catalog
Delta Lake
MLflow
Experience owning Databricks workspace architecture and governance
Infrastructure, Automation & Reliability
Deep experience with Terraform/Terramate (or similar IaC tools)
Proven experience building CI/CD pipelines (GitHub Actions, Jenkins)
Advanced Python and Bash scripting skills