Databricks
About this role
Databricks runs its data and AI platform across AWS, Azure and GCP for more than 10,000 organisations, and the Multi Cloud Efficiency team exists to make sure every dollar of that cloud spend is optimised. The posting is candid about the scale of the problem: the team engineers systems that optimise the footprint as Databricks scales into the hundreds of millions of dollars in cloud spend. Concretely you would design and build systems handling high volume data streams to produce accurate cost attribution and automated resource management, eliminate inefficiencies in the cloud architecture, and build tooling that makes cloud economics legible to other engineers and budget owners. There is an automation and agentic angle too: building tooling that detects waste, attributes cost and remediates issues with minimal human intervention. It is backend infrastructure work with an unusually direct line to the company's margins.
Who this is for
What the posting requires:
- 6+ years of experience, including producing high quality production code and detailed technical design documents for distributed systems.
- Proven ability to break down complex, multi month projects into actionable milestones and tasks.
- Solid grasp of industry best practice for distributed systems: monitoring, documentation, testing and finish quality.
- Experience optimising cloud resource utilisation or developing large scale distributed tools.
- Ability to make well reasoned trade offs between system performance, development velocity and technical debt.
What the work actually looks like:
- Lead projects from concept to deployment, designing and building scalable systems that handle high volume data streams for cost attribution and resource management.
- Identify and eliminate inefficiencies in the cloud architecture, engineering solutions that mitigate resource waste and improve attribution accuracy.
- Act as technical expert and owner for your service area across its full lifecycle, including code quality, monitoring and operational overhead.
- Work with product and infrastructure teams to turn business needs into technical specifications, and build tooling that simplifies cloud economics for other engineers.
- Contribute to code reviews, share engineering practice and mentor junior engineers.
- Drive a shift left culture by integrating efficiency checks directly into the developer workflow.
- Build automation and AI or agentic tooling that detects waste, attributes cost and remediates issues with minimal human intervention.
The scale described: the team manages infrastructure efficiency across AWS, Azure and GCP for one of the world's largest data and AI platforms, with cloud spend in the hundreds of millions of dollars.
Location and working pattern: Bengaluru. Databricks says it is still building out this site, so expect an in office or hybrid arrangement. The number of days is not stated.
Compliance note in the posting: where a role requires access to export controlled technology or source code, Databricks says applying for a US government licence is at its discretion and it may decline to proceed on that basis.
Honest fit guidance: cost and efficiency engineering is often dismissed as unglamorous, but at this scale it is a genuine distributed systems problem with a measurable outcome. If you have built large scale tooling and enjoy owning a service end to end, six years is a fair bar.
- 6+ years of experience, including producing high quality production code and detailed technical design documents for distributed systems.
- Proven ability to break down complex, multi month projects into actionable milestones and tasks.
- Solid grasp of industry best practice for distributed systems: monitoring, documentation, testing and finish quality.
- Experience optimising cloud resource utilisation or developing large scale distributed tools.
- Ability to make well reasoned trade offs between system performance, development velocity and technical debt.
What the work actually looks like:
- Lead projects from concept to deployment, designing and building scalable systems that handle high volume data streams for cost attribution and resource management.
- Identify and eliminate inefficiencies in the cloud architecture, engineering solutions that mitigate resource waste and improve attribution accuracy.
- Act as technical expert and owner for your service area across its full lifecycle, including code quality, monitoring and operational overhead.
- Work with product and infrastructure teams to turn business needs into technical specifications, and build tooling that simplifies cloud economics for other engineers.
- Contribute to code reviews, share engineering practice and mentor junior engineers.
- Drive a shift left culture by integrating efficiency checks directly into the developer workflow.
- Build automation and AI or agentic tooling that detects waste, attributes cost and remediates issues with minimal human intervention.
The scale described: the team manages infrastructure efficiency across AWS, Azure and GCP for one of the world's largest data and AI platforms, with cloud spend in the hundreds of millions of dollars.
Location and working pattern: Bengaluru. Databricks says it is still building out this site, so expect an in office or hybrid arrangement. The number of days is not stated.
Compliance note in the posting: where a role requires access to export controlled technology or source code, Databricks says applying for a US government licence is at its discretion and it may decline to proceed on that basis.
Honest fit guidance: cost and efficiency engineering is often dismissed as unglamorous, but at this scale it is a genuine distributed systems problem with a measurable outcome. If you have built large scale tooling and enjoy owning a service end to end, six years is a fair bar.
Apply on company site
Opens databricks.com, the employer's own application page. Applying is always free.