Senior Data Engineer, Manager

This is Us:

JS Perkins Consulting (JSPC) delivers value added management and technology consulting services. JSPC is committed to creating trusted partnerships to provide sustainable solutions to meet our client needs. We thrive on respect for the individual and host an open idea meritocracy.

J.S. Perkins Consulting is seeking a Senior Data Engineer to support a mission-critical Defense Health data modernization initiative.

The program is building an enterprise data orchestration layer across a large, federated defense health data landscape. The work connects clinical, operational, financial, and readiness source systems to a modern enterprise data platform, then curates that raw data into governed, well-documented silver and gold data products that mission users can trust and reuse. The model is federated: data stays with its authoritative owners, and the orchestration layer makes it discoverable, interoperable, and consumable through catalogs, APIs, and a data product marketplace.

In this role you will serve as a senior technical contributor, delivering production-ready data capabilities while establishing reusable patterns for ingestion, integration, and delivery. You will take on the harder integration problems, help shape how ingestion and curation are done consistently across pipeline and product teams, and support other engineers working through unfamiliar systems. This is a senior individual contributor role with real technical depth, not a management position.

• Medical/Dental/Vision
• 401K (up to 5% match)
• 11 Paid Holidays
• 4-5 weeks PTO (depending on level)
• Bonus Incentives
• Professional Development
• Telework
• Continuing Education Credit

• Design, build, and operate scalable, production-ready data pipelines and data products using Apache Spark with Python and SQL in a Databricks environment
• Lead the integration and transformation of complex data from diverse defense and federal health systems into reliable, reusable silver and gold data products
• Design scalable approaches for data ingestion, integration, and exchange across a federated data landscape, 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, and integration approaches
• Define and implement data validation, quality, and governance practices, including data quality rule frameworks and anomaly detection, that make data products trustworthy at every layer
• Optimize Delta Lake and Databricks performance for large-scale, high-volume workloads
• Troubleshoot complex technical and data integration challenges, identify root causes, and drive durable solutions rather than repeat fixes
• Partner with subject matter experts and functional owners to decode complex business logic and convert domain knowledge into defensible technical data requirements
• Ensure data products carry the metadata, lineage, and documentation required for enterprise catalog registration and customer data governance expectations
• Provide technical guidance and mentorship to other engineers, helping the team navigate complex or unfamiliar technical challenges
• Proactively identify opportunities to improve engineering tooling, process, and patterns, and help drive their adoption
• Collaborate with engineers, architects, analysts, and customer stakeholders to translate complex data needs into scalable technical solutions

• 8 or more years of data engineering, software engineering, or closely related technical experience
• Extensive hands-on experience designing, building, and operating production data pipelines and data products at scale
• Expert-level SQL. Able to write, optimize, and debug complex queries for large-scale analysis and transformation, including complex joins, window functions, and common table expressions
• Advanced proficiency with Python
• Strong experience with Apache Spark and distributed data processing across clusters
• Deep familiarity with the Databricks platform, including workspace management, notebook collaboration, and Delta Lake optimization
• Experience designing and maintaining ETL and 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, including establishing data quality rules and anomaly detection
• Experience troubleshooting and optimizing complex production pipelines for performance, reliability, and scalability
• Experience with Git-based development workflows, code review, and modern software engineering practices
• Comfort working in terminal and command-line environments
• Demonstrated experience providing technical guidance, mentoring engineers, and influencing engineering practice
• Ability to…