Data Engineer

LMI•Scott AFB, IL

14h•$76,390 - $130,605•Onsite

About The Position

LMI is currently seeking a Data Engineer to analyze, manage, and provide insights from organizational databases across USTRANSCOM, located at Scott AFB, IL. This on-site position requires a combination of robust data management expertise and advanced analytical skills to design and implement efficient data systems while uncovering actionable insights that drive strategic decision-making. 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, health-care, and energy sectors—helping agencies navigate complexity and out-pace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value. We offer a generous compensation package with excellent benefits that start the first day of employment. Come join the organization consistently recognized as a top workplace!

Requirements

Bachelor's Degree in Data Science, Information Computer Science, Data Analytics, Computer Science, or a related field of study.
Active Secret clearance (preferred) or ability to obtain one (required).
Experience with Databricks, Palantir Foundry, Snowflake, Microsoft Fabric, Apache Spark, Kafka, Airflow, or similar modern data platforms.
Experience implementing data mesh, data fabric, or domain-oriented data architectures.
Familiarity with OpenAPI specifications, API gateways, and microservices architectures.
Experience supporting AI/ML data pipelines and feature engineering workflows.
Knowledge of data cataloging and governance tools.
Experience working in Agile, DevSecOps, or product-oriented delivery environments.
Experience supporting Department of Defense (DoD), federal, or highly regulated environments.
Familiarity with SQL and one or more programming languages such as Python, Java, Scala, or R.
Experience developing ETL/ELT pipelines and data integration workflows.
Working knowledge of one or more modern data and analytics platforms, such as Databricks, Qlik, Palantir, or similar technologies.
Experience working with and producing meaningful datasets from relational, non-relational and unstructured data.
Experience developing and consuming APIs.
Knowledge of data modeling, data warehousing, and metadata management concepts.
Experience with cloud platforms such as AWS, Azure, or Google Cloud.
Understanding of data governance, security, and quality principles.

Nice To Haves

Experience with data visualization and reporting tools to communicate insights visually.
Experience working with a data warehouse environment preferred.
Proficiency in designing dimensional databases and advanced object/data modeling.

Responsibilities

Responsible for designing, building, and maintaining scalable data platforms, data products, and application programming interfaces (APIs) that enable secure, reliable, and reusable access to enterprise data.
This role transforms raw and disparate data sources into consumable products that support analytics, artificial intelligence (AI), machine learning (ML), operational decision-making, and digital modernization initiatives.
Works closely with business stakeholders, architects, software engineers, data scientists, and product owners to translate mission and business requirements into robust data solutions.
The position emphasizes Data-as-a-Product principles, API-first design, data governance, automation, and cloud-native engineering practices.
Data Product Development: Design, develop, and maintain enterprise data products that provide trusted, discoverable, and reusable data assets for consumers across the organization.
Implement Data-as-a-Product principles, including data ownership, quality, documentation, discoverability, and lifecycle management.
Define and maintain data contracts, schemas, and interface specifications to ensure consistency and interoperability.
Develop and manage metadata, data catalogs, lineage, and governance artifacts supporting enterprise data management.
Data Engineering & Integration: Design and implement scalable data ingestion, transformation, and processing pipelines using modern ETL/ELT methodologies.
Integrate structured, semi-structured, and unstructured data from multiple internal and external sources.
Build and optimize relational, dimensional, and analytical data models that support reporting, business intelligence, operational systems, and advanced analytics.
Maintain a strong understanding of modern data architectures, including data warehouses, data lakes, lakehouses, and data meshes, and leverage that knowledge to guide planning, integration, governance, and operational activities.
Implement data quality controls, validation frameworks, monitoring, and automated testing.
API & Data Services Development: Design, develop, and maintain secure data access and integration services that enable consumers to discover, access, and interact with enterprise data products through standardized interfaces and event-driven mechanisms.
Create reusable data services that enable application integration, analytics, and operational workflows.
Implement API versioning, documentation, security controls, rate limiting, and performance optimization.
Support API lifecycle management and integration with enterprise service platforms and gateways.
Enable data interoperability across cloud, on-premises, and hybrid environments.
Architecture & Solution Design: Translate business and mission requirements into scalable data architectures and technical solutions.
Collaborate with enterprise, solution, and system architects to align data solutions with organizational strategies and technology roadmaps.
Work collaboratively to validate logical and physical data models, reference architectures, and implementation patterns.
Evaluate and recommend technologies, platforms, and frameworks supporting modern data ecosystems.
Cloud & Platform Engineering: Leverage cloud-based data services and platform capabilities to develop, manage, and deliver scalable data products that meet consumer and mission requirements.
Apply functional knowledge of cloud architectures, services, and deployment models to support the integration, operation, and evolution of enterprise data ecosystems.
Design and optimize data pipelines, storage solutions, and processing workflows using cloud-native capabilities to improve data quality, accessibility, performance, and cost efficiency.
Collaborate with platform, security, and DevSecOps teams to ensure data products are deployed, monitored, secured, and maintained in accordance with enterprise standards.
Support implementation of automated testing, deployment, observability, and governance practices that enhance the reliability and usability of data products.
Data Governance & Security: Apply data governance, data management, and cybersecurity best practices throughout the data lifecycle.
Ensure compliance with organizational, regulatory, and security requirements.
Implement appropriate access controls, encryption, auditing, and data protection mechanisms.
Support data stewardship and governance activities across the enterprise.