Mission-Critical Data Solutions Engineer (Onsite TS/SCI)

IBM Computing; Alexandria (VA); On-site; USD 130,000 - 180,000; Full time

Job summary

IBM is seeking a hands-on Data Solutions Engineer to turn raw, incomplete data into usable, actionable insights in a secure, mission-focused environment. You will work across data, systems, and analysis to deliver outputs that support decision making in real conditions and sit on-site in the Washington, DC metro area.

You will collaborate with junior data engineers and data scientists, build data pipelines, apply models, and integrate internal and external models while balancing technical

Qualifications

• Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical field or equivalent practical experience.
• 5+ years of experience in data science, machine learning, or applied data roles.
• Strong proficiency in Python and working knowledge of SQL.

Responsibilities

• Work directly with raw and incomplete data, determining how to structure, process, and use it effectively.
• Develop and apply models and analytical methods to identify meaningful patterns, behaviors, and signals.
• Build and support data pipelines that enable consistent, repeatable processing across datasets.
• Integrate internal and external models into working systems and adjust them as conditions or data changes.
• Operate effectively in secure environments with tradeoffs and constraints.

Skills

Python

SQL

Data analysis

Data modeling

Education

Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical field

Tools

APIs

Containerization

Job description

IBM is seeking a hands-on Data Solutions Engineer to turn raw, incomplete data into usable, actionable insights in a secure, mission-focused environment. You will work across data, systems, and analysis to deliver outputs that support decision making in real conditions and sit on-site in the Washington, DC metro area.

You will collaborate with junior data engineers and data scientists, build data pipelines, apply models, and integrate internal and external models while balancing technical