Databricks
About this role
Databricks runs its data and AI platform across AWS, Azure and GCP, and the Multi Cloud Efficiency team exists to make sure the money spent on all that infrastructure is not wasted. This is a genuinely unusual engineering problem: reserved instance automation, tag based cost attribution at scale, automated waste cleanup policy engines and cross cloud resource governance, on a cloud footprint measured in hundreds of millions of dollars. You would be technical lead for the Bengaluru chapter of that team, which the posting says has already delivered millions of dollars in verified annual savings within two quarters of forming. Part of the brief is building automated release gates that block changes which regress agreed efficiency targets, so other teams feel your work directly.
Who this is for
What the posting requires: 9 or more years building production grade distributed systems in Java, Scala, C++ or Go. A proven track record architecting solutions for large scale infrastructure and cloud compute. The role is described as technical lead for the Bengaluru chapter, defining technical strategy for problems where the solution is not yet known.
The day to day: architecting systems to optimise cloud spend at very large scale; defining the roadmap for the efficiency pillar and running design reviews and risk assessments; building regression detection and automated resource rightsizing; building the frameworks that let product teams measure their own efficiency footprint, and the automated release gates that hold them to it; acting as technical authority during incidents involving system wide performance issues; mentoring junior and senior engineers; and designing the AI driven detection, decisioning and autonomous remediation layers that let cost governance run continuously.
Location and office reality: Bengaluru, India. Databricks describes itself as still building out this office, so expect a growing team rather than an established one.
Who this is for: a distributed systems engineer who finds infrastructure economics interesting rather than tedious. It is a good seat if you want measurable impact you can point at in a performance review, since the output is denominated in dollars saved. The founding engineer framing means more ambiguity and more say in direction than a typical Staff role.
The day to day: architecting systems to optimise cloud spend at very large scale; defining the roadmap for the efficiency pillar and running design reviews and risk assessments; building regression detection and automated resource rightsizing; building the frameworks that let product teams measure their own efficiency footprint, and the automated release gates that hold them to it; acting as technical authority during incidents involving system wide performance issues; mentoring junior and senior engineers; and designing the AI driven detection, decisioning and autonomous remediation layers that let cost governance run continuously.
Location and office reality: Bengaluru, India. Databricks describes itself as still building out this office, so expect a growing team rather than an established one.
Who this is for: a distributed systems engineer who finds infrastructure economics interesting rather than tedious. It is a good seat if you want measurable impact you can point at in a performance review, since the output is denominated in dollars saved. The founding engineer framing means more ambiguity and more say in direction than a typical Staff role.
Apply on company site
Opens databricks.com, the employer's own application page. Applying is always free.