Leidos has an immediate need for a highly skilled and versatile Data Engineer, Sr. to drive the massive data discovery and classification effort for the Zero Trust initiative at U.S. Special Operations Command (USSOCOM). Before data can be protected, it must be found and understood. You will be responsible for illuminating "dark data" across the Command's complex information environment, ranging from hyperscale cloud data lakes on NIPR to legacy file shares and isolated storage arrays on the SIPR and Top-Secret networks.
As a Data Engineer, Sr., you will architect and manage the deployment of advanced discovery platforms, specifically BigID and NetApp BlueXP. You will configure these tools to crawl petabytes of structured (SQL/Oracle), semi-structured (logs/NoSQL), and unstructured (SharePoint/File Shares) data. Your primary mission is to build the "Global Data Inventory"-a dynamic, real-time map of where sensitive CUI and classified intelligence resides-enabling the security teams to apply precision protection. You will use your knowledge of data pipelines and storage infrastructure to ensure that scanning operations provide 100% visibility without degrading network performance.
Responsibilities:
- Data Discovery Architecture: Deploy and manage BigID and NetApp BlueXP scanners across hybrid environments, including configuring dockerized collectors for air-gapped discovery on the Top-Secret network.
- Structured Data Mapping: Connect discovery tools to enterprise databases (SQL Server, Oracle, PostgreSQL) to scan schemas and columns for PII, DoD ID numbers, and other sensitive indicators without impacting database performance.
- Unstructured Data Crawling: Configure scans for massive file repositories (NetApp NAS, QNAP, SharePoint On-Premises), optimizing scan windows and throttling to prevent latency for mission users.
- Cloud Data Integration: Utilize Microsoft Purview Data Map and custom connectors to inventory data residing in AWS S3 buckets, Azure Blobs, and Data Lakes.
- Classification Tuning: Collaborate with mission owners to train Machine Learning (ML) classifiers to recognize unique USSOCOM data types (e.g., mission names, operational codes) and reduce false positive rates in the data inventory.
Qualifications:
Security Clearance: Active Top-Secret clearance with SCI eligibility (TS/SCI).
Education: Master's degree in Systems Engineering, Computer Science, Cybersecurity, Electrical Engineering, or a related technical field.
Experience: 10+ years of prior relevant experience.
Certification: CompTIA Security+ CE (or higher) to meet DoD 8570 IAT Level II requirements.
Preferred Qualifications:
Preferred Experience/Skills:
- Experience with Microsoft Purview Data Map (formerly Azure Purview).
- Knowledge of Regex for custom data pattern definition.
- Background in Data Engineering or ETL pipeline development.
- Familiarity with USSOCOM network architecture and storage standards.
Preferred Certifications:
- Certified Kubernetes Administrator (CKA).
- Azure Data Engineer Associate or BigID Certified Professional.
Come break things (in a good way). Then build them smarter.
We're the tech company everyone calls when things get weird. We don't wear capes (they're a safety hazard), but we do solve high-stakes problems with code, caffeine, and a healthy disregard for "how it's always been done."
Original Posting:December 29, 2025
For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.
Pay Range:Pay Range $131,300.00 - $237,350.00
The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.
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