Data Engineer

Data Engineer

The Data & Analytics group at PNNL is seeking an early‑career Data Engineer to support and enhance the Laboratory's enterprise data platforms. This role is responsible for maintaining the reliability and performance of the legacy Data Warehouse while contributing to the multi‑year transition toward an Azure Databricks lakehouse architecture. The successful candidate will possess a solid foundation in SQL and a demonstrated interest in enterprise data operations. Working under the guidance of experienced mentors, the Data Engineer will engage in structured development activities, operational troubleshooting, documentation improvements, and incremental modernization tasks. This position offers a clear growth pathway and hands-on experience with both established and emerging data engineering technologies and practices.

Key Responsibilities:

• Support day-to-day operations of our production Data Warehouse with guidance from senior engineers
• Maintain and enhance existing SQL-based logic (queries, views, and stored procedures) used to curate and publish trusted datasets.
• Assist with data modeling and documentation (e.g., basic dimensional modeling concepts, lineage, and dataset definitions).
• Support DW-to-lakehouse migration work (Azure Databricks) by helping with data mapping, validation, and reconciliation
• Collaborate with technical peers and stakeholders to clarify requirements and deliver reliable data outputs.
• Work in an agile environment: manage assigned work, participate in standups/sprint activities, and communicate progress and blockers.
• Grow your technical breadth —starting with DW support and expanding into Databricks, automation, and modern pipeline patterns as readiness and project needs align.

Minimum Qualifications:

• BS/BA and 2 years of relevant experience -OR-
• MS/MA -OR-
• PhD

Preferred Qualifications:

• Working knowledge of SQL (writing queries with joins and aggregations; ability to troubleshoot unexpected results).
• Experience with relational databases and/or data warehouse concepts
• Basic understanding of data concepts such as datasets, transformations, and data quality.
• Ability to collaborate, communicate status and blockers, and follow documented processes
• Exposure to agile ways of working and using a work-tracking tool such as Jira (or similar).
• Exposure to dimensional modeling (facts/dimensions, star schemas) and downstream reporting use cases.
• Experience with stored procedures, scripting, job scheduling/orchestration, or production support practices.
• Familiarity with cloud data platforms and/or lakehouse concepts; exposure to Azure and/or Databricks is a plus.
• Python experience (for data processing, automation, testing, or validation).
• Experience supporting data migration activities (source-to-target mapping, reconciliation, validation).
• Interest in pursuing Databricks training and/or certification within a 2–3 year horizon.