Engineering Manager - Compute Infra

Databricks
Mountain View, California; San Francisco, CaliforniaPosted 7 April 2026

Job Description

P-931 At Databricks, we are passionate about helping data teams solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. At Databricks, the Compute Infrastructure organization builds and operates the foundation that runs all Data, AI and stateful workloads across all major clouds. Our platform launches tens of millions of VMs per day, operates thousands of Kubernetes clusters, and must deliver extreme elasticity, reliability and cost efficiency. We are looking for an Engineering Manager to lead a team responsible for critical components of this platform. Your work will directly influence product velocity, customer experience, and the company’s ability to scale. The impact you will have: Own and evolve the compute platform to support all Databricks workloads , enabling engineers to build world-class products with high velocity and best-in-class performance. Hire exceptional engineers and invest in their growth through coaching, feedback, and career development. Raise the technical and operational bar through strong design practices, testing, and a culture of engineering excellence and platform mindset . Partner with engineering and product leadership to define long-term strategy and roadmaps. Lead cross-team initiatives that span product and infrastructure surface areas. Influence architectural decisions beyond your immediate team. What we look for: 10+ years of experience building and operating large-scale distributed systems , including strong practices around testing, monitoring, and reliability. 3+ years of engineering management experience leading high-performing teams. Deep expertise in cloud infrastructure (AWS, Azure, GCP) and containerized environments (Kubernetes) is preferred. Proven ability to collaborate across engineering, product, and business stakeholders. Experience scaling teams and systems through rapid growth. BS, MS, or PhD in Computer Science or related field, or equivalent experience. Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . Local Pay Range $190,000 $253,750 USD About Databricks Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter , LinkedIn and Facebook . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For speci ... (truncated, view full listing at source)
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