Design, develop, and maintain scalable, production-ready data pipelines and data products using Spark (Python/SQL) in a Databricks environment Lead the integration and transformation of complex data from diverse DoW and federal health systems and sources into reliable, reusable data products Design scalable approaches for data ingestion, integration, and exchange, including API-based integrations and services Establish and promote reusable data engineering patterns, standards, and best practices that improve consistency, scalability, and maintainability across data products Provide technical guidance on data architecture, pipeline design, data modeling, integration approaches, and engineering practices Monitor, troubleshoot, and optimize production workflows and data pipelines, identifying performance, reliability, and scalability improvements Define and implement data validation, quality, and governance practices that improve the reliability and usability of data products Troubleshoot complex technical and data integration challenges, identify root causes, and drive sustainable solutions Collaborate with engineers, architects, analysts, and customer stakeholders to translate complex data needs into scalable technical solutions Provide technical guidance and mentorship to other engineers, helping teams navigate complex or unfamiliar technical challenges Proactively identify opportunities to improve engineering tools, processes, and patterns and help drive their adoption across the team Take ownership of complex technical areas and help maintain engineering quality, consistency, and cohesion as the platform and portfolio of data products grow REQUIRED SKILLS & EXPERIENCE 10+ years of data engineering, software engineering, or related technical experience Extensive hands-on experience designing, building, and operating production data pipelines and data products Advanced proficiency with Python and SQL Strong experience with Apache Spark and distributed data processing Experience working with Databricks or similar modern data platforms Experience designing and maintaining ETL/ELT processes for complex, large-scale datasets Experience integrating data across disparate systems and consuming or developing API-based data integrations Strong understanding of data modeling, data architecture, data quality, and data governance principles Experience troubleshooting and optimizing complex production data pipelines for performance, reliability, and scalability Experience working with Git-based development workflows and modern software engineering practices Experience working in terminal / command-line environments Demonstrated experience providing technical guidance, mentoring engineers, and influencing engineering practices Strong client and stakeholder communication skills, with the ability to translate technical concepts and recommendations for both technical and non-technical audiences Ability to independently navigate ambiguity, identify technical risks, and drive complex engineering challenges toward resolution Experience spotting security, privacy and compliance issues and working with security/compliance/legal stakeholders NICE TO HAVE SKILLS & EXPERIENCE AWS cloud experience Experience working with very large datasets, including datasets with billions of records Experience with Palantir Foundry GitLab experience Experience working with Advana or similar DoW data environments Experience working with federal health, financial, or other regulated and sensitive data Experience using AI/ML to accelerate work, including automating routine tasks, accelerating development and debugging
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