Big Data Engineer Principal

Job ID: 2611025

Location: Orlando, FL, US

Date Posted: 2026-04-06

Category: Information Technology

Subcategory: Big Data Engineer

Schedule: Full-Time

Shift: Day Job

Travel: Yes - 10% of the time

Minimum Clearance Required: Secret

Clearance Level Must Be Able to Obtain: None

Potential for Remote Work: ORA_ON_SITE

Description

SAIC is seeking an experienced Big Data / Parametric Data Engineer to support an Air Force program out of Orlando, FL.

(This is an onsite position, with no option for remote work).

You must possess an Active Security Clearance to qualify for this position.

Job Duties / Responsibilities:

• Ensure parametric datasets established for supporting training and readiness comply with the Department of the Air Force (DAF) Data Fabric initiatives as well as the DoD Data Strategy, DAF I-Plan, and AFAMS Data Concept Plan.
• Provision parametric data services for Air Force Training Simulation Centers:
• Leverage commercial technology to the maximum extent possible and shall align work with the existing DAF Data Fabric initiative, to include the mandatory use of one of the top five big data platforms: Envision, VAULT, ELICSAR, Unified Data Library, and SIGNUS.
• Test data services for the community to access parametric data addressing an agreed upon Source of Truth (ASoT) Dataset for the Training Simulation Centers.
• Provision data services for the community to access parametric data; Make data: Visible, Accessible, Usable, Linked, Trusted, Interoperable, and Secure for the AF Training Simulation Centers.
• Support tactical data training requirements:
• Support government training data efforts at the tactical level to meet integrated training needs.
• Support joint/coalition interoperability data efforts to enhance training capabilities.
  
  

Qualifications

• Bachelor's and nine (9), Masters and seven (7); or relevant years of experience in lieu of degree
• Demonstrable expertise or be a subject matter expert in DoD wide parametric data.
• Data access capability using SQL and python.
• Knowledge of commercial parametric data sources.
• Experience using military parametric data in synthetic environments.
• Ability to train others on the usage of parametric data.
• Data Engineering skill building ETL (Extract-Transform-Load) pipelines to support self-service data capability to the developer.
• Data Interoperability acumen focused on making many siloed data systems work as one.
• Semantic modeling and Ontology to support future graph database implementation.
• Well Rounded understanding of metadata and its implementation in a data catalog.
• Develop trusted authoritative datasets and data products that support varied use-cases.
• Understanding the deployment of air-gapped data systems.
• Solid understanding of data fabric and data mesh design and architectures.
• Wide understanding of legacy and current data systems.
• US Citizen and an Active Secret Security Clearance
  
  

Desired Experience:

• Data Expert(s) with demonstrated proficiency (e.g., Data Analyst, Data Scientist, Data Engineer, etc.) and the most current Information Technology (IT) and IT/Data mecca experience (e.g., Silicon Valley, Austin, Seattle, Colorado Springs).
• Individual(s) must have previous experience in designing/building data stores or connecting via existing commercial Application Programming Interfaces (APIs) to commercially available data stores and must have previous experience in planning data lakes to transform and load legacy datasets to enterprise datasets for cloud environments.
• Individual(s) must be familiar with the DAF’s Data Fabric effort with one of the top five big data platforms: Envision, VAULT, ELICSAR, Unified Data Library, and SIGNUS as starting points for DAF enterprise data transport, data mapping, data prep, and/or data aggregation effort.
• Individual(s) must be Certified Data Management Professionals (CDMP) accredited by the Data Management Association (DAMA) International.
• Individual(s) shall have first-hand experience with data services platforms to control data assets and guide the aggregation of large and disparate collections of enterprise data.
• Individual(s) must have hands-on experience demonstrating, within one project, the ability to handle large data assets across various commercial industries that created new opportunities previously not realized.