Director of Engineering Data Infrastructure
Databricks is a data and AI platform that lets organizations build analytics, AI agents, and applications on a unified, governed lakehouse.
Funding history
Investors
About Databricks
Data engineers, analysts, and AI teams use Databricks to process large datasets, build reliable pipelines, and train models on a single governed platform. Users can run SQL analytics, serve ML predictions in real time, and deploy AI agents grounded in enterprise data. Its open lakehouse architecture provides consistent security and governance across analytical and operational workloads.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will build and lead a data infrastructure organization in Bengaluru. You will define infrastructure strategy, hire engineering managers, deliver reliable billing, recovery, observability, testing, ingestion, and deployment platforms, and position infrastructure as a product for internal engineering customers.
Requirements
- 14+ years in distributed systems engineering
- 6+ years leading infrastructure organizations
- 4+ years managing managers
- Petabyte-scale data pipelines and distributed systems reliability
- Multi-region disaster recovery architecture
- Multi-year infrastructure strategy and roadmap development
- SLOs, SLIs, chaos engineering, disaster recovery, and observability
- Experience scaling infrastructure organizations
- Executive and cross-functional communication
- Apache Spark, Delta Lake, large-scale data infrastructure, fintech or billing systems, or hypergrowth infrastructure leadership
Responsibilities
- Deliver infrastructure systems for billing correctness, disaster recovery, testing, and observability
- Build the Bengaluru data infrastructure organization and hire engineering managers
- Own business-critical systems operating across regions
- Drive reliability improvements and eliminate operational toil through automation
- Ship correctness, deployment automation, data integration, and testing platforms
- Treat internal engineering teams as customers and measure infrastructure adoption and satisfaction