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$48K

$111.8K

$166K

How much do databricks software jobs pay per year?

As of Sep 13, 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 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.

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Databricks Software jobs near you
Infographic showing various Databricks Software job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 86% Full Time, 9% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution, with an average salary of $111,845 per year, or $53.8 per hour.

Principal Databricks Software Developer

Atlanta, GA • On-site

PowerPlan, Inc
Software Development • 201 - 500 employees
Learn more about PowerPlan, Incexternal

Other

Posted 23 days ago

Job description

Overview
PowerPlan is seeking a Senior Principal Databricks Platform Engineer to take ownership of the health, reliability, performance, governance, and cost-effectiveness of our Databricks platform - not from the sidelines, but hands-on with the notebooks, pipelines, clusters, and Unity Catalog work that shapes how teams build.
Within your first quarter, you'll be designing the standards teams adopt. Within six months, teams will be bringing you their toughest design decisions before they build, not after something breaks. This is a senior, visible role where the problems are real, and your judgment on reliability, performance, and cost will directly shape how PowerPlan runs Databricks going forward.
About PowerPlan
PowerPlan helps the companies that power the world unlock greater value from their infrastructure investments. We combine deep industry expertise, trusted technology, and AI-driven innovation to help capital-intensive organizations manage the financial complexity of their assets with confidence.
We're in the middle of one of the most significant transformations in our history. Decades of proven functionality are being reimagined on a modern SaaS foundation, while AI becomes central to how we build, deliver, and evolve our products. The next generation of the PowerPlan platform is being designed right now. Join us, and you'll have the opportunity to influence the technology, engineering practices, and capabilities that will shape it for years to come.
Responsibilities
Your Impact
  • Audit the platform's highest-priority assets - clusters, jobs, pipelines, notebooks, and access controls - and deliver an evidence-backed picture of the biggest risks within 30 days.
  • Own the design of critical Databricks standards - compute policies, Spark configuration, notebook and pipeline patterns, and Unity Catalog governance - and get teams to actually adopt them.
  • Serve as the escalation point for complex Databricks failures, performance regressions, and cost anomalies, digging to root cause every time.
  • Turn every fix into something reusable - better defaults, templates, reference architectures, and automation instead of one-off patches.
  • Become the person teams check in with before they build, guiding responsible adoption of AI-assisted engineering practices along the way.
What Success Looks Like
Within 30 days, you've given leadership a real, evidence-based picture of platform risk instead of guesswork. Within a quarter, at least one platform standard you designed is showing up in how teams actually build. By six months, teams bring you their toughest design decisions before they build - not after something breaks - and recurring anti-patterns are visibly declining.
Qualifications
What You'll Bring
  • 8+ years in data engineering, platform engineering, or related software engineering roles, including 3+ years of deep hands-on Databricks experience.
  • Production experience with Spark internals and performance tuning - shuffle behavior, query planning, adaptive query execution, memory pressure, skew, and partitioning.
  • Strong working knowledge of Databricks compute options, cluster policies, instance pools, autoscaling, Photon, and workload right-sizing.
  • A track record of reviewing and improving notebooks, jobs, and pipelines built by other people, quickly spotting what's driving runtime, cost, or reliability risk.
  • Production experience with Delta Lake, Structured Streaming, Auto Loader, and data quality practices.
  • Strong command of Unity Catalog governance, lineage, permission models, and secure handling of sensitive data.
  • Experience designing resilient job orchestration, including dependency management, retries, timeouts, and alerting.
  • Strong communication skills, including the judgment to translate technical risk into terms stakeholders can act on.
Education & Experience
  • Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent professional experience.

PowerPlan is an EOE
Applicant and Candidate Privacy Notice
Please note that this is a hybrid role that involves a combination of onsite work from our corporate office as well as work from home. While we strive to accommodate flexible working arrangements when sensible, there will be times when onsite work is required. This could include scheduled office days, team meetings, client meetings, or special events.

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