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Databricks Software Jobs (NOW HIRING)

TheStaffed

Senior Software Engineer - Databricks

New York, NY · On-site

$134K - $176K/yr

... Software Engineer with 8-10 years of experience to rebuild and develop applications for the Direct Investment Unit, focusing on data pipelines and platform modernization using Databricks while ...

Databricks

Sr Software Engineer -Public Sector

Mclean, VA · On-site

$155K - $214K/yr

P-1366 About Databricks At Databricks, we are passionate about enabling data teams to solve the ... As a Software Engineer on our Public Sector team, you will be responsible for the core backend ...

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How much do databricks software jobs pay per year?

As of Aug 23, 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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Cities with the most Databricks Software job openings:

What states have the most Databricks Software jobs?

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What job categories do people searching Databricks Software jobs look for?

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Databricks Software jobs near you
Infographic showing various Databricks Software job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 87% Full Time, 8% Part Time, and 4% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $111,845 per year, or $53.8 per hour.

Principal Databricks Software Developer

PowerPlan, Inc

Atlanta, GA • On-site

Other

Posted 2 days ago

New

Job description

Overview
PowerPlan is a profitable market leader with over 30 years of experience in asset-intensive financial systems and managing trillions of dollars of clients' assets. PowerPlan is in a growth phase investing into next-generation solutions. We're hiring a Principal Databricks Platform Engineer to take ownership of the health, reliability, performance, governance, and cost effectiveness of our Databricks platform.
This is not a role for someone who only advises from the sidelines. You'll be hands-on with notebooks, pipelines, jobs, clusters, Spark configurations, Delta Lake patterns, and Unity Catalog. You'll be the person teams turn to when something technically works but is slower than it should be, more expensive than it needs to be, harder to operate than it should be, or fragile enough to become tomorrow's production issue.
Responsibilities
  • Take ownership of the highest-risk Databricks workloads and platform patterns, from configuration review through remediation and production behavior.
  • Review existing clusters, jobs, notebooks, pipelines, and workspace settings to identify cost waste, performance bottlenecks, reliability risks, security gaps, and governance drift.
  • Set technical direction for Databricks usage across teams, including compute policies, Spark configuration standards, notebook design expectations, pipeline patterns, orchestration practices, and Unity Catalog governance.
  • Raise the bar for Databricks code and architecture reviews by identifying anti-patterns such as oversized clusters, unnecessary shuffles, driver-side collections, unbounded full scans, brittle job dependencies, missing retry logic, poor Delta maintenance, and hardcoded environment assumptions.
  • Partner with engineering teams to turn findings into practical improvements: better defaults, reusable templates, reference architectures, automation, monitoring, and clear standards that teams can actually follow.
  • Serve as an escalation point for complex Databricks failures, performance regressions, pipeline instability, data quality issues, and cost anomalies.
  • Translate technical risk into business impact so stakeholders understand why a slow job, missing lineage, weak guardrail, or inefficient cluster design matters.
  • Make engineers around you better through pairing, reviews, design conversations, internal workshops, and examples that demonstrate what excellent Databricks engineering looks like.
What success looks like
One month in: you've reviewed the most important Databricks assets, shipped or guided meaningful production improvements, built a real mental model of the platform, and can name the biggest architectural, operational, and cost risks with evidence.
Three months in: you own the design and improvement plan for at least one critical Databricks capability or platform standard, and your recommendations are showing up in how teams build, review, schedule, and operate Databricks workloads.
Six months in: teams seek your input before building high-risk workloads, not after they fail. Platform standards are clearer, recurring anti-patterns are declining, performance and reliability trends have improved, and Databricks cost decisions are being made with discipline instead of guesswork.
Qualifications
  • 8+ years in data engineering, platform engineering, or related software engineering roles, with at least 3+ years of deep hands-on Databricks experience.
  • Production experience with Spark internals and performance tuning, including shuffle behavior, query planning, adaptive query execution, memory pressure, skew, spill, partitioning, and broadcast strategy.
  • Strong working knowledge of Databricks compute options, cluster policies, job clusters, all-purpose clusters, instance pools, autoscaling, serverless eligibility, Photon, and workload right-sizing.
  • Real experience reviewing and improving notebooks, jobs, and pipelines that other people built, including the ability to quickly identify the few design choices causing most of the runtime, cost, or reliability risk.
  • Production experience with Delta Lake, Delta maintenance strategies, Structured Streaming, Auto Loader, materialized views or streaming tables, and data quality expectations.
  • Strong understanding of Unity Catalog, workspace governance, lineage, permission models, catalog/schema organization, table documentation, and secure handling of sensitive data.
  • Experience designing reliable job orchestration: task decomposition, dependency management, retries, repairs, timeouts, alerting, scheduling, max concurrent runs, and recoverability.
  • A track record of identifying systemic reliability, performance, governance, or cost issues and driving practical remediation across teams.
  • Strong communication skills, including the judgment to explain technical findings in a way that helps engineering leaders, product owners, and stakeholders make better decisions.
  • A STEM degree, or equivalent depth earned through substantial production experience.
Also valuable
  • Experience with Azure-based data platforms and enterprise cloud operations.
  • Experience partnering with FinOps or platform teams to reduce cloud spend without sacrificing reliability or service-level expectations.
  • Experience building reusable Databricks templates, platform automation, internal standards, or reference architectures.
  • Familiarity with data privacy, security, auditability, and regulated business environments.
  • Opinions, backed by evidence, about when Spark or Databricks is the right tool - and when it is not.
  • Experience helping teams adopt AI-assisted engineering practices responsibly, especially for code review, test generation, documentation, and operational analysis.
Why this role
You'll shape how PowerPlan uses Databricks for critical data workloads, and your judgment will directly influence reliability, performance, governance, and cost. This is a senior, hands-on role where the work is visible, the problems are meaningful, and the best answer is not always obvious.
The right person will look at something that "works" and see what is fragile, wasteful, insecure, or difficult to operate before it becomes an incident. You'll set the standard for Databricks technical excellence, and the improvements you drive will make the platform stronger for every team that depends on it.
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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