Data Engineer (Supply Chain) - Clearance Required
LMI is hiring a remote Data Engineer to integrate and analyze parts, supplier, procurement, inventory, and risk data supporting US government industrial base and supply chain decisions. Day to day: building SQL/Python data pipelines, matching inconsistent identifiers (NSNs, CAGE codes), and producing traceable analytical outputs. Security clearance eligibility required.
- Compensation
- $108.3K–$160.7K / yr
- Location
- Remote, US
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Contract
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Experience integrating, transforming, and analyzing data from multiple sources
- Proficiency in SQL and Python or comparable data processing tools
- Experience designing structured datasets, data pipelines, and quality assurance checks
- Ability to resolve inconsistent identifiers and document matching methods and exceptions
- Experience producing analytical outputs usable by nontechnical stakeholders
- Strong collaboration and communication skills, including working with subject matter experts
- Ability to document assumptions, data provenance, and analytical limitations
- Eligibility for a U.S. Government security clearance (US citizens only)
Nice to have
- Experience with defense logistics, procurement, inventory, or supplier datasets
- Familiarity with NSNs/NIINs, CAGE codes, bills of materials, and assembly hierarchies
- Experience with entity resolution, graph analysis, or supply network mapping
- Experience developing lead-time, capacity, or schedule models with domain experts
Full description
Overview
LMI is seeking a Supply Chain Data Analyst / Engineer to integrate and analyze complex parts, supplier, procurement, inventory, and risk data in support of industrial base decisions. This individual will build repeatable workflows that connect an indentured parts list to relevant government and commercial data, resolve inconsistent identifiers, and make analytical findings traceable to their sources. Working closely with supply chain analysts and technical experts, the Data Analyst / Engineer will help the team identify potential constraints at scale and focus manual investigation where it will have the greatest value. The ideal candidate combines strong data engineering and analytical skills with an interest in defense logistics and supply chain challenges.
LMI is a new breed of digital solutions provider dedicated to accelerating government impact with innovation and speed. Investing in technology and prototypes ahead of need, LMI brings commercial-grade platforms and mission-ready AI to federal agencies at commercial speed.
Leveraging our mission-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors—helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value.
Responsibilities
- Ingest, clean, transform, and integrate parts, assembly, supplier, procurement, inventory, and risk datasets.
- Develop methods to match part numbers, NSNs/NIINs, CAGE codes, drawing identifiers, and other inconsistent records.
- Build and maintain a structured assessment dataset that connects parts to higher assemblies, sources, evidence, risks, and actions.
- Automate repeatable screening and data quality checks while routing ambiguous matches for analyst review.
- Document data lineage, refresh dates, matching logic, and confidence measures.
- Develop analytical queries and views that surface long lead times, supplier concentration, stock-outs, delivery delays, and other potential constraints.
- Partner with domain experts to develop and validate lead-time, capacity, or schedule estimates where appropriate.
- Support rolling updates as new parts lists, procurement records, and supplier information become available.
- Translate user needs into practical data products and explain the limitations of the underlying data.