Principal Engineer - Databricks

Flexera saves customers billions of dollars in wasted technology spend. A pioneer in Hybrid ITAM and FinOps, Flexera provides award-winning, data-oriented SaaS solutions for technology value optimization (TVO), enabling IT, finance, procurement and cloud teams to gain deep insights into cost optimization, compliance and risks for each business service. Flexera One solutions are built on a set of definitive customer, supplier and industry data, powered by our Technology Intelligence Platform, that enables organizations to visualize their Enterprise Technology Blueprint™ in hybrid environments—from on-premises to SaaS to containers to cloud.

We’re transforming the software industry.  We’re Flexera.  Withmore than 50,000 customersacross the world, we’re achievingthat goal.Butwe knowwe can’t do any of that without our team. Ready to help us re-imagine the industry during a time of substantial growth and ambitious plans?  Come and see whywe’re consistently recognized by Gartner, Forrester and IDCas a category leader in the marketplace. Learn more atflexera.com

At Flexera, we empower global enterprises by turning IT insights into action. We’re seeking a Principal Engineer with deep expertise in architecting and optimizing big data platforms, with a strong focus on Databricks. You’ll drive the core design and performance of our lakehouse, enabling analytics, AI/ML, and product innovation at scale.
As a senior technical leader, you’ll own key components of our data platform — collaborating with architects, data scientists, and engineers to deliver high-performance, scalable solutions. The ideal candidate is a Databricks expert, fluent in modern data engineering, distributed systems, and cloud-native architectures.

What you'll do:

• Architect, design, and implement scalable and performant Databricks-based data pipelines, including batch and streaming workloads.

• Optimize Databricks Lakehouse architecture — including Delta Lake, Unity Catalog, and Photon — for cost efficiency, data reliability, and query performance at scale.
• Define and implement advanced data models and ontologies to support complex analytics and AI/ML workflows.
• Design and tune complex Spark applications (PySpark and/or Scala) for high-volume data processing, including structured streaming and incremental processing.
• Implement orchestration workflows (e.g., Databricks Workflows, Azure Data Factory, or similar) to automate data pipelines with robust monitoring and alerting.
• Drive best practices for data governance, security, and lineage, leveraging Unity Catalog, table ACLs, and audit capabilities.
• Optimize cluster configurations, autoscaling strategies, and cost management within Databricks environments.
• Collaborate with data scientists and analytics engineers to enable self-service access to high-quality, well-documented datasets and features.
• Lead complex troubleshooting efforts, deep performance tuning, and root-cause analysis across distributed systems.
• Author and maintain technical artifacts, including architecture diagrams, design docs, reference implementations, and best practice guides.
• Mentor and coach engineers across teams on Databricks internals, Spark performance tuning, and modern data engineering architectures.
• Stay on top of emerging Databricks features (e.g., Mosaic AI, serverless compute, Delta Live Tables), advocating for their adoption when they bring tangible value.

You'll be expected to have:

• Bachelor’s or higher degree in Computer Science, Software Engineering, or a related field.
• 12+ years of software engineering experience, with 6+ years specifically in building large-scale data platforms.

• Expert-level proficiency in Databricks, including:

• Delta Lake internals (ACID transactions, time travel, Z-ordering, data compaction strategies)
• Unity Catalog for data governance, security, and lineage
• Advanced Spark tuning (shuffle management, partitioning strategies, caching, cluster configuration)
• Databricks SQL Warehousing, Workflows, and MLflow integration
• Strong expertise with cloud-native data engineering in Azure or AWS, including storage (ADLS/S3), networking, and IAM integration.
• Knowledge of streaming architectures (Apache Kafka, structured streaming), schema evolution, and event-driven data modeling.
• Hands-on experience with metadata catalogs, orchestration frameworks, and observability tooling (e.g., Datadog, Grafana).
• Strong understanding of distributed systems, fault tolerance, and large-scale performance engineering.
• Proven ability to solve complex, ambiguous problems and make architectural trade-offs in real-world production systems.
• Excellent technical communication skills — ability to document, present, and explain technical concepts clearly to varied audiences.
• Prior experience mentoring engineers, conducting design reviews, and setting technical direction across teams.
• A continuous learner mindset — actively keeping pace with evolving Databricks platform capabilities and broader data engineering trends.

Preferred but not required:

• Experience with Power BI Direct Lake mode and semantic modeling for lakehouse-based analytics.
• Familiarity with Mosaic AI, Databricks GenAI capabilities, or Feature Store implementations.
• Contributions to open-source Spark, Databricks community content, or certifications (e.g., Databricks Certified Data Engineer Professional).
   

At Flexera, we foster a fun and engaged hybrid working environment where collaboration and innovation thrive. We value diversity and encourage applicants from underrepresented groups in technology to apply.

Join our team to not only contribute to a world-class global product but also to grow in your career. At Flexera, we encourage continuous learning and provide opportunities for professional development.