performance engineer

Ai. Databricks
United States
12 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Compensation
$166,000.0 - $225,000.0
Working hours
Regular working hours

Tech stack

Artificial Intelligence Data Analysis Computing Platforms Cloud Computing Distributed Systems Cloud Services Software Engineering Virtual Machines Data Processing Concurrency Apache Spark Information Technology
+2 more
Low Latency Databricks

Job description

At Databricks, we are passionate about enabling data teams to solve the world’s toughest problems. 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. We constantly push the boundaries of data and AI technology, while simultaneously operating with the resilience, security and scale that is critical to making customers successful on our platform. Databricks develops and operates one of the largest scale software platforms; the fleet consists of millions of virtual machines, generating terabytes of logs and processing exabytes of data per day. At our scale, we regularly observe cloud hardware, network, and operating system faults, and our software must gracefully shield our customers from any of the above.

As a performance engineer, you will work closely with multiple teams across the company to evaluate the performance of products and features, identify performance bottlenecks, and partner with engineers to solve performance and scalability issues. This implies, among other teams, setting performance targets for various software releases, guiding teams to develop performance benchmarks, running competitive benchmark analysis for different Databricks products, doing deep dive analysis to identify performance issues and fix them.

The impact you will have:

  • Identify performance limitations of the entire stack based on telemetry, customer signals, PoCs, and competitive benchmarks, that will result in the best performing system across the industry, when resolved. Dimensions include latency, data and compute scalability, concurrency, cost, and price to performance ratio. Impact spans all cloud providers and all major areas.
  • Set the performance expectations for all cross-cutting efforts early on through specialized benchmarks capturing the intended customer user journeys, and make sure they are met before deployed to customers.
  • Understand the performance characteristics of the compute instance types, storage layers, and all cloud services Databricks depends on and deploy optimal solutions to meet the customer demand.
  • Work with customers to root cause and mitigate performance problems during production, previews, and POCs.

Requirements

  • BS (or higher degree) in Computer Science, or a related field
  • Experience in the performance analysis discipline. Ability to identify performance issues, root cause problems, and be able to come up with potential solutions.
  • Experience in software development, preferably in large scale distributed systems
  • Ability to measure and document the impact of performance features to existing customers, such as possible regressions for certain workloads, their extent, and which customers will be affected.
  • Ability to build strong working relationships with developers and field engineers to facilitate triaging and mitigation of performance problems.

Pay Range Transparency, Analysis Skills, Apache Spark, Artificial Intelligence (AI), Benchmarking, Cloud Computing, Competitive Analysis/Strategy, Computer Science, Concurrency, Data Analysis, Data Processing, Distributed Computing, Diversity, Documentation, Equal Employment Opportunity (EEO), Facebook, Fortune 500 Customers, Identify Issues, Large-Scale Systems, LinkedIn, Operating Systems, Performance Analysis, Performance Engineering, Problem Solving Skills, Process Improvement, Sales/Support Engineering (SE), Software Development, Software Engineering, Team Player, Telemetry, Vehicle Fleets, Virtual Machine (VM)

Benefits & conditions

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

$166,000-$225,000 USD

About Databricks, At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.

About the company

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.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.careerbuilder.com

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

3:09 min

Balancing data science skillings alongside systems engineering rigor

Nico Schmidt · LIVE

2:27 min

Managing traffic and tracking costs with Databricks Unity Catalog

Viktoria Semaan Viktoria Semaan · WWC Europe 2026

5:59 min

Analyzing concurrency bottlenecks in standard serverless architectures

Marco Plaul Marco Plaul +1 · WWC 2023

5:48 min

Balancing delivery latency with stream reliability and scale

Phil Cluff · LIVE

1:59 min

Key takeaways and accessing the Databricks developer toolkit

Viktoria Semaan Viktoria Semaan · WWC Europe 2026

3:03 min

Career evolution in data engineering and AI platforms

Maria Apazoglou · Coffee With Developers

Videos

See all

Related articles

See all