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Databricks Software Jobs in Mountain View, CA (NOW HIRING)

Databricks

Sr Software Engineer-Networking

Mountain View, CA

$144K - $190K/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

Staff Software Engineer - IAM

Mountain View, CA · On-site

$190K - $274K/yr

P-1126 Summary At Databricks, we are obsessed with enabling data teams to solve the world's toughest problems, from security threat detection to cancer drug development. We do this by building and ...

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Databricks Software information

See Mountain View, CA salary details

$56.6K

$131.9K

$195.8K

How much do databricks software jobs pay per year?

As of Sep 8, 2026, the average yearly pay for databricks software in Mountain View, CA is $131,941.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,200.00 and $153,400.00 per year, depending on experience, location, and employer.

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 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 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.

What job categories do people searching Databricks Software jobs in Mountain View, CA look for?

The top searched job categories for Databricks Software jobs in Mountain View, CA are:

What cities near Mountain View, CA are hiring for Databricks Software jobs?

Cities near Mountain View, CA with the most Databricks Software job openings:

Databricks Software jobs near you
Infographic showing various Databricks Software job openings in Mountain View, CA as of August 2026, with employment types broken down into 1% Internship, 82% Full Time, 14% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $131,941 per year, or $63.4 per hour.

Staff Software Engineer - Distributed Data Systems

San Francisco, CA

Databricks
Software Development • 5 - 10K employees
Learn more about Databricksexternal

Full-time

Re-posted 15 days ago

Job description

P-186

At Databricks, we are obsessed with enabling data teams to solve the world's toughest problems, from security threat detection to cancer drug development. We do this by building and running the world's best data and AI infrastructure platform, so our customers can focus on the high value challenges that are central to their own missions.

Founded in 2013 by the original creators of Apache Spark, Databricks has grown from a tiny corner office in Berkeley, California to a global organization with over 1000 employees. Thousands of organizations, from small to Fortune 100, trust Databricks with their mission-critical workloads, making us one of the fastest growing SaaS companies in the world.

Our engineering teams build highly technical products that fulfill real, important needs in the world. 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.

We develop and operate 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.

Modern data analysis employs sophisticated methods such as machine learning that go well beyond the roll-up and drill-down capabilities of traditional SQL query engines. As a software engineer on the Runtime team at Databricks, you will be building the next generation distributed data storage and processing systems that can outperform specialized SQL query engines in relational query performance, yet provide the expressiveness and programming abstractions to support diverse workloads ranging from ETL to data science.

Below are some example projects:

Apache Spark: Develop the de facto open source standard framework for big data.

Data Plane Storage: Deliver reliable and high performance services and client libraries for storing and accessing humongous amount of data on cloud storage backends, e.g., AWS S3, Azure Blob Store.

Delta Lake: A storage management system that combines the scale and cost-efficiency of data lakes, the performance and reliability of a data warehouse, and the low latency of streaming. Its higher level abstractions and guarantees, including ACID transactions and time travel, drastically simplify the complexity of real-world data engineering architecture.

Delta Pipelines: It's difficult to manage even a single data engineering pipeline. The goal of the Delta Pipelines project is to make it simple and possible to orchestrate and operate tens of thousands of data pipelines. It provides a higher level abstraction for expressing data pipelines and enables customers to deploy, test & upgrade pipelines and eliminate operational burdens for managing and building high quality data pipelines.

Performance Engineering: Build the next generation query optimizer and execution engine that's fast, tuning free, scalable, and robust.

What we look for:

  • BS in Computer Science, related technical field or equivalent practical experience.
  • Optional: MS or PhD in databases, distributed systems.
  • Comfortable working towards a multi-year vision with incremental deliverables.
  • Driven by delivering customer value and impact.
  • 8+ years of production level experience in either Java, Scala or C++.
  • Strong foundation in algorithms and data structures and their real-world use cases.
  • Experience with distributed systems, databases, and big data systems (Apache Spark, Hadoop).

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