AI/ML Data Solutions: Partner with AI/ML developers to deliver data ready for model training and deployment. You'll build feature stores, create model-ready datasets, and design ontology models that give structure and meaning to complex mission data. Your work enables data scientists and engineers to move faster—from raw data to deployed AI capabilities.
Full Lifecycle Data Engineering: Own the entire data solution lifecycle. You'll profile and analyze data, create conceptual, logical, and physical data models, and architect ETL processes that turn unorganized data into analytics-ready sources. Working through requirements gathering to production deployment, you'll define functional capabilities, security specifications, backup and recovery strategies, and performance optimization approaches.
Classified Program Operations: Work within secure facilities and classified environments, obtaining required clearances and following all security and authentication protocols. You'll implement the controls and mechanisms required to protect sensitive data while enabling teams to move quickly and deliver value.
Basic Qualifications
Degree in Computer Science, Systems Engineering, or related field
2-4 years of experience in data engineering or related data/analytics roles
1+ years of experience supporting AI/ML systems or data science teams
Understanding of traditional business intelligence and data warehousing concepts (ETL, data modeling, and reporting)
Proficiency with Python and SQL databases, demonstrated through coursework, projects, internships, or professional experience
Understanding of foundational data engineering concepts, including relational databases, data modeling, ETL or ELT pipelines, and data quality
Ability to analyze problems, learn new technologies, and communicate clearly in a collaborative team environment
Ability to work in an Agile development environment and use version-control tools such as Git
Able to obtain special program clearance
Active U.S. Government Secret Security Clearance
Desired Skills
- Familiarity with SAP HANA, Neo4j, Eqube or similar data integration, graph database, and data pipeline tools
- Exposure to AI or machine-learning workflows, including data preparation for model training and analysis
- Familiarity with ontologies, taxonomies, knowledge graphs, or semantic data modeling
- Master's degree in a related technical field or progress toward advanced degree
Pay Information
Full-Time Salary Range: $70500.00 - $130900.00
At Lockheed Martin, we know mission success starts with taking care of our people. Our Total Rewards program is designed to attract top talent, support your well-being, and help you grow—both professionally and personally.
The salary range for this position is as listed on the requisition. Please note that the salary information listed is a general guideline only. Lockheed Martin considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/ training, key skills as well as market(work location) and business considerations when extending an offer.
Benefits offered: Medical, Dental, Vision, Flexible work arrangements and schedules (e.g., 4x10), 401(k) match, Paid time off, Holidays, Parental Leave, EAP, Flexible Spending Accounts, Education Assistance, Life Insurance, Short-Term Disability, and Long-Term Disability.
Annual short-term and/or long-term incentive compensation programs may be offered depending on the position. Payments under these annual programs are not guaranteed and can vary from year to year and are tied to a range of performance metrics.
For (Washington state applicants only) Non-represented full-time employees: accrue at least 10 hours per month of Paid Time Off (PTO) to be used for incidental absences and other reasons; receive at least 90 hours for holidays. Represented full time employees accrue 6.67 hours of Vacation per month; accrue up to 52 hours of sick leave annually; receive at least 96 hours for holidays. PTO, Vacation, sick leave, and holiday hours are prorated based on start date during the calendar year.
