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Databricks Software Jobs (NOW HIRING)

Databricks

Senior Software Engineer - Backend

Bellevue, WA · On-site +1

$138.30K - $182.30K/yr

P-939 (Position Location is open to both our Seattle & Bellevue offices.) At Databricks, we are ... As a software engineer with a backend focus, you will work with your team to build infrastructure ...

Databricks

Senior Software Engineer - Backend

Bellevue, WA · On-site

$157.70K - $213.80K/yr

As a software engineer with a backend focus, you will work with your team to build infrastructure ... Build Databricks serverless platform that powers the big data, machine learning and Gen AI ...

Databricks

Senior Software Engineer - Money Team

Bellevue, WA · On-site

$157K - $225.90K/yr

At Databricks, we are obsessed with Data + AI to solve the world's toughest problems, from security ... As a Sr. Software engineer on the Money team, you will be closely involved in the entire billing ...

Databricks

Sr Software Engineer-Networking

Bellevue, WA · On-site

$157K - $213.80K/yr

(P-1286) At Databricks, we are passionate about enabling data teams to solve the world's toughest ... We are seeking experienced Senior Software Engineers with large-scale distributed system experience ...

Databricks

Senior Software Engineer - Money Team

Bellevue, WA

$138.30K - $182.30K/yr

At Databricks, we are obsessed with Data + AI to solve the world's toughest problems, from security ... As a Sr. Software engineer on the Money team, you will be closely involved in the entire billing ...

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How much do databricks software jobs pay per year?

As of May 30, 2026, the average yearly pay for databricks software in the United States is $111,845.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,000.00 and $130,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Databricks Software Engineer, and why are they important?

To thrive as a Databricks Software Engineer, you need strong programming skills in languages like Python, Scala, or Java, as well as a solid understanding of distributed computing and data engineering concepts. Familiarity with Databricks platform, Apache Spark, cloud services (such as AWS or Azure), and relevant certifications like Databricks Certified Data Engineer are highly valued. Excellent problem-solving abilities, collaboration, and effective communication are important soft skills for this role. These skills ensure efficient development, deployment, and optimization of big data solutions that drive business insights and innovation.

What are some common challenges faced by Databricks Software Engineers, and how can they be overcome?

Databricks Software Engineers often encounter challenges related to scaling big data pipelines, optimizing Spark workloads, and integrating diverse data sources. Navigating the complexity of distributed systems and managing cloud infrastructure can be demanding, especially when ensuring data reliability and security. To overcome these challenges, engineers typically collaborate closely with data scientists, DevOps, and platform teams, leverage Databricks' extensive documentation and community support, and adopt best practices such as version control and continuous integration. Regular knowledge sharing and staying updated with new features also help engineers succeed in this dynamic environment.

What is Databricks Software?

Databricks Software is a unified analytics platform built on Apache Spark that provides tools for big data processing, machine learning, and collaborative data science. It enables organizations to store, manage, and analyze large datasets efficiently, supporting both batch and streaming data workloads. Databricks also offers collaborative notebooks, automated workflows, and integrations with cloud storage and data lakes, making it a popular choice for data engineering, data science, and business analytics teams.

What is the difference between Databricks Software vs Data Engineer?

AspectDatabricks SoftwareData Engineer
Primary RolePlatform for data analytics and machine learningBuilds, maintains data pipelines and infrastructure
Required SkillsSQL, Spark, cloud platforms, data science basicsSQL, ETL, programming (Python, Scala), database management
Work EnvironmentCloud-based, collaborative data platformData teams, cloud or on-premises environments
CertificationsDatabricks certifications, cloud certificationsNone specific, often cloud or data certifications

While Databricks Software provides a platform for data analytics and machine learning, Data Engineers focus on building and maintaining data pipelines and infrastructure. Both roles often work together but have distinct responsibilities and skill sets within the data ecosystem.

More about Databricks Software jobs
What cities are hiring for Databricks Software jobs? Cities with the most Databricks Software job openings:
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What job categories do people searching Databricks Software jobs look for? The top searched job categories for Databricks Software jobs are:
Databricks Software jobs near you
Infographic showing various Databricks Software job openings in the United States as of May 2026, with employment types broken down into 95% Full Time, and 5% Contract. Highlights an 96% Physical, and 4% Hybrid job distribution, with an average salary of $111,845 per year, or $53.8 per hour.
Sr. Software Engineer - Performance

Sr. Software Engineer - Performance

Databricks

San Francisco, CA

$164.90K/yr

Other

Posted 4 days ago

Job description

P-97

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.

What We Look For:

  • 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.

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