| Location | South Jordan, UT |
Databricks Performance and FinOps Engineer
Job Summary
The Databricks Performance and FinOps Engineer makes the platform faster and cheaper at the same time, and can prove both. This role owns compute strategy, workload tuning, and the cost visibility that lets leadership see where spend is going and why. It is a good fit for engineers who enjoy the forensic side of the work and can hold a defensible position when someone wants to solve a performance problem by buying more compute.
Key Responsibilities
Analyze workload and cluster telemetry to find the jobs driving disproportionate cost or runtime
Tune Spark jobs and SQL workloads through partitioning, caching, join strategy, file layout, and skew handling
Define compute strategy across job clusters, serverless, SQL warehouses, and pools
Build cost allocation and chargeback reporting using system tables, tags, and budget policies
Set and enforce cluster policies and autoscaling configuration to prevent avoidable spend
Establish performance baselines and service level objectives for critical pipelines
Run cost reviews with engineering and finance stakeholders and translate technical drivers into budget language
Advise on photon adoption, runtime upgrades, and instance type selection
Required Skills
5+ years in data engineering or platform engineering with a focus on performance
Deep Spark tuning experience and fluency reading the Spark UI, query profiles, and execution plans
Hands on experience with Databricks system tables and usage analytics
Understanding of cloud pricing models for compute and storage across at least one major cloud
Strong SQL and Python
Ability to build reporting that non technical stakeholders will actually use
Preferred Skills
FinOps Certified Practitioner credential
Experience with liquid clustering, predictive optimization, and photon workload assessment
Prior ownership of a platform cost reduction program with documented results
Experience with tagging strategy and enterprise cloud cost tooling
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