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Databricks

Builds cloud software for data analysis and AI

Staff Software Engineer - Multi Cloud Efficiency

Backend EngineeringConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • At least 9 years of experience building production-grade distributed systems in Java, Scala, C++, or Go.
  • A proven track record of architecting solutions for large-scale infrastructure, cloud computing across AWS, Azure, or GCP, and container orchestration with Kubernetes or Docker.
  • Deep expertise in identifying and solving performance bottlenecks in high-throughput distributed environments.
  • Demonstrated ability to drive technical requirements for ambiguous problems where the path forward is not obvious.
  • T-shaped engineering skills, with depth in distributed systems and breadth across cloud infrastructure, monitoring, and software lifecycles.

Responsibilities

  • Serve as the technical lead for the Bengaluru MCE chapter and define technical strategy for efficiency projects with unknown solutions.
  • Lead cross-functional efforts with finance and cost teams to address large-scale engineering challenges and deliver cost-efficient systems for global, multi-cloud data processing while maintaining performance and stability.
  • Lead the design and implementation of systems to optimize cloud spend, including solutions for reserved-instance automation, tag-based cost attribution at scale, automated waste-cleanup policy engines, and cross-cloud resource governance.
  • Identify and deliver high-impact, well-adopted projects and define the roadmap for the efficiency pillar, including conducting design reviews and risk assessments for systems supporting core infrastructure.
  • Design scalable systems for regression detection and automated resource rightsizing.
  • Build frameworks that enable product teams to measure their services’ efficiency footprint and use automated release gates to block changes that regress agreed efficiency targets (SLOs).
  • Serve as a technical authority for incident management and address complex, system-wide performance issues.
  • Improve the stability and reliability of internal platform tools so they can support growing global scale.
  • Mentor junior and senior engineers, raise engineering quality, and drive engineering culture across the organization.
  • Design automation and agentic layers for AI-driven detection, decisioning, and autonomous remediation so cost governance can operate continuously at scale.

About the Company

Databricks

Builds cloud software for data analysis and AI

Databricks builds cloud software that businesses use to organize data, analyze it, and develop AI applications. Its lakehouse architecture brings the flexible storage of a data lake together with the querying and management tools of a data warehouse. Data engineers prepare and update datasets, analysts build reports, and developers train models or create applications in a shared workspace with controls over who can use the data. The platform runs on Amazon, Microsoft, and Google clouds, and customers pay for computing usage or commit to longer contracts for discounted rates. Databricks was founded in 2013 and is headquartered in San Francisco, California.

Company size
10,001+
Company stage
Late Stage VC
Total funding
$32.1B
Headquarters
San Francisco, CA
Founded
2013

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