Principal Databricks Software Developer

Overview

We're looking for a Senior Principal Databricks Platform Engineer to take ownership of the health, reliability, performance, governance, and cost-effectiveness of PowerPlan's 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.

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

Success will be measured by how quickly you turn platform risk into an evidence-based picture leadership can act on, and by how often teams start bringing you their toughest design decisions before they build.

• 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.

Qualifications

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• 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.