Data Engineer - Mid

Leidos•Reston, VA

1d•Onsite

About The Position

The Intel Security Sector at Leidos currently has an opening for a Data Engineer. This role involves delivering advanced data engineering capabilities to support national security objectives, swiftly produce and analyze results, disseminate findings, and contribute to presentations with actionable intelligence insights. The successful candidate will work on projects to design, develop, and deploy big data analytic and visualization products to support DIA/NDOC OSINT production on behalf of the Defense Intelligence Enterprise (DIE). This position will be located at one of our customer sites in Bethesda, MD; Reston, VA; or Joint Base Anacostia-Bolling, DC.

Requirements

Bachelor’s degree in Computer Science, Data Science, Engineering, Information Systems, or related technical discipline and 4 years of experience. Additional years of experience in lieu of degree are acceptable.
Candidates with a minimum of 3 years of recent experience in the field will be considered.
Active TS/SCI clearance with the ability to obtain and maintain TS/SCI with Polygraph.
Experience developing and maintaining data pipelines using modern programming languages (e.g., Python, Java, or similar).
Experience working with relational and non-relational databases.
Experience performing data ingestion, transformation, and processing tasks.
Experience working with data tools and platforms (e.g., Spark, Kafka, Airflow, or similar).
Experience operating within Agile or SAFe environments supporting enterprise systems.

Nice To Haves

At least 8 years of experience conducting analysis relevant to the specific labor category with at least a portion of the experience within the last 2 years.
Bachelor’s degree in an area related to the labor category from a college or university accredited by an agency recognized by the U.S. Department of Education.
A significant share of your experience in direct support of military or intelligence community customers, demonstrating progressive technical development and mission-focused outcomes.
Significant experience dealing with at least two of the following data classes: open source, publicly available information (PAI); forensic media (i.e., DOMEX); measurement and signatures intelligence (MASINT); or cybersecurity and cyber forensics
Experience supporting data pipelines across multi-enclave DoD environments.
Experience working with cloud-based data platforms (AWS, Azure, or GCP).
Experience supporting AI/ML data preparation and feature engineering workflows.
Experience working with containerized environments (Docker, Kubernetes).
Familiarity with data governance, metadata management, and lineage concepts.
Experience creating dashboards and prototype web applications with technologies such as Kibana, Tableau, of Power BI.
Current or recent SCI-level access is a significant advantage and preferred.
Successful polygraph within the last 5 years is a significant advantage and preferred.

Responsibilities

Work closely with a tight-knit team of data scientists, as well as a larger team of software developers, network engineers, senior investigators, program managers, researchers, and data analysts to produce and clean large databases, provide data processing, ensure data quality, integrate large data with applications, prepare data visualizations and produce dashboards using state of the art tools, and AI/ML techniques.
Apply software engineering and ETL principles to ensure data accuracy, quality, consistency, and scalability.
Integrate COTS and customer-developed tools within existing data frameworks to meet operational and analytical requirements.
Collaborate with DataOps teams to prepare, automate, and optimize data workflows for real-time analytics.
Work on projects analyzing a variety of big data covering national security, cyber security, business intelligence, online social media, human behavior and more.
Support multiple simultaneous projects and take open-ended or high-level guidance, independently and collaboratively.
Provide data preprocessing, quality checks, data integration, and visualization creation.
Develop dashboards with tools like Tableau or Power BI and apply AI/ML modules to structured and unstructured data analysis.