Senior Databricks Engineer

Pacific Northwest National Laboratory•UNAVAILABLE, OH

19h•Remote

About The Position

PNNL’s Digital Platforms organization is modernizing the enterprise data ecosystem to accelerate mission delivery across national security, energy, and scientific research. We operate robust, cloud native data platforms that enable analysts, scientists, and technologists to work faster and smarter. We are seeking an experienced Databricks Engineer to support the design, build, and operation of our enterprise data lakehouse that powers analytics and reporting across PNNL systems. This role will focus on delivering governed, reliable, and performant data products—especially for ERP and other enterprise sources—and enabling downstream analytics with Power BI and AI/ML. As part of a data transformation initiative, you will support the design, develop and maintain Databricks based lakehouse solutions that moves data reliably from source systems to curated gold tables. Using modern patterns such as Medallion Architecture, Delta Lake and Unity Catalog, you’ll support the build of scalable pipelines that transform raw data into analytics ready assets for Power BI and AI/ML, balancing pragmatic delivery with future-focused architecture. You’ll support the modernization of legacy data warehouses and ETL tools into Databricks, transforming legacy ETL processes into scalable, maintainable solutions. Your platform engineering mindset will shape CI/CD for Databricks (e.g., DAB, GitHub Actions) and standardize deployment practices across environments. You will configure and operate workspaces, clusters, jobs, and workflows; tune for performance and reliability; and embed data quality, monitoring, and observability to keep critical pipelines healthy. Security and governance will be integral to your work. You’ll implement role-based access controls, data masking, and Unity Catalog models to ensure secure, compliant data sharing, proper classification, lineage, and auditability. You’ll stay current with Azure and Databricks capabilities, recommending and piloting features like Delta Live Tables and Unity Catalog enhancements to build a secure, reliable, and future ready data platform that accelerates science and mission delivery. As part of our WE Culture, we offer a collaborative and agile team environment that embraces learning and fosters mentoring opportunities.

Requirements

PhD and 1 years of Data or Software Engineering experience -OR- MS/MA and 3 years of Data or Software Engineering experience -OR- BS/BA and 5 years of Data or Software Engineering experience -OR- AA and 14 years of Data or Software Engineering experience -OR- HS/GED and 16 years of Data or Software Engineering experience
At least 1 years of experience developing production Databricks solutions.
Experience in coding languages, such as, Python and SQL in a Spark/Databricks environment.
Experience with cloud services (platforms like AWS, Azure, GCP).

Nice To Haves

Experience working with Delta Lake, SQL, notebooks, end to end pipeline development, and cluster management.
Experience implementing Databricks Asset Bundles (DAB) or equivalent for CI/CD and standardizing deployment workflows.
Exposure to agentic AI / AI agents (e.g., orchestrating multi-step AI workflows for data quality checks, pipeline monitoring, or support automation) is a plus.
Familiarity with core Azure services such as ADLS Gen2 and Key Vault
Experience using GenAI / LLM-based tools (e.g., GitHub Copilot, Azure OpenAI, Databricks Genie, or similar) to accelerate and automate engineering tasks such as code generation, test creation, documentation, and troubleshooting.

Responsibilities

Design and build scalable Databricks lakehouse solutions using Medallion Architecture, Delta Lake, Unity Catalog, Spark/PySpark, Python, and SQL.
Operate and optimize production Databricks environments, including Jobs/Workflows, clusters, orchestration, CI/CD, DAB, and core Azure services.
Modernize legacy warehouses and ETL processes and develop reliable ingestion-to-gold pipelines for ERP and complex transactional data.
Implement secure, governed data platforms with access controls, lineage, data quality, monitoring, observability, and performance optimization.
Apply GenAI/LLM and emerging AI-agent technologies to automate engineering, testing, documentation, data quality, monitoring, and support.