Data Engineer, Senior Consultant

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 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.

This is a hands-on build role. You will work directly in Spark and Databricks to land data from clinical and operational source systems, transform it into curated silver and gold datasets, and make those datasets reliable enough that mission users trust them without checking your work. The right candidate is strong in SQL, comfortable in distributed processing, disciplined about data quality, and willing to sit down with a subject matter expert to figure out what a messy source field actually means.

• 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 maintain production data pipelines using Apache Spark with Python and SQL in a Databricks environment
• Ingest and integrate data from diverse defense and federal health source systems across clinical, operational, financial, and readiness domains
• Transform raw source data into curated silver and gold data products with documented business logic, clear ownership, and consistent definitions
• Build and maintain API-based integrations for ingesting and exchanging data across federated systems and platforms
• Implement data quality controls, including rule creation, anomaly detection, and validation checks that hold data integrity and consistency across every layer of the architecture
• Optimize Delta Lake tables and Databricks workloads for performance, cost, and reliability
• Monitor, troubleshoot, and tune scheduled workflows and production pipelines, resolving failures and performance issues at the root cause
• Produce and maintain the metadata, lineage, and documentation needed to register data products in the enterprise data catalog and marketplace
• Engage subject matter experts directly to decode complex business logic and translate domain knowledge into technical data requirements
• Contribute to code reviews, automated testing, version control practices, and reusable engineering patterns that raise consistency across the team
• Manage multiple concurrent data work streams in a fast-paced delivery environment and communicate status, risks, and blockers early

• 3 to 10 years of data engineering, software engineering, or closely related technical experience, with 5 or more years preferred
• 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
• Strong hands-on experience with Apache Spark, processing large datasets efficiently across distributed clusters
• Hands-on experience with the Databricks platform, including workspace management, notebook collaboration, and Delta Lake optimization
• Strong proficiency with Python
• Demonstrated experience building, monitoring, and maintaining robust end-to-end ETL and ELT pipelines in production
• Solid understanding of data quality and data governance practice, including creating data quality rules and implementing anomaly detection to protect data integrity and consistency
• Working knowledge of data modeling and turning raw data into reusable…