Data Engineer (SC Cleared)
- Compensation
- Not disclosed
- Location
- United Kingdom
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Strong experience in Python, SQL, and TypeScript.
- Solid grounding in data engineering, ETL pipelines, and workflow orchestration.
- Experience with enterprise data platforms used in operational settings (Palantir experience is a strong plus, but not mandatory).
- Comfort operating in complex, high‑pressure client environments.
- Interest in applied AI / machine learning with a practical mindset.
- Clear communicator who can bridge technical and non‑technical audiences.
- Curious, adaptable, and delivery‑focused.
Full description
This range is provided by Fortice. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range
Direct message the job poster from Fortice
Founder @ Fortice and Forza Consulting | Headhunter for UK & EU Defence Tech, AI & National Security | Building High-Trust Teams
About the OpportunityFortice are recruiting on behalf of a fast-growing AI and data engineering scale-up operating on complex, mission-critical programmes for government and enterprise clients.
The business sits at the intersection of data engineering, advanced analytics, and applied AI, delivering solutions that support real operational decision‑making in high‑stakes environments. The company has recently entered its next phase of growth following a strategic acquisition by a global consulting and technology partner, bringing scale and long‑term programme stability while retaining an engineering‑led, delivery‑focused culture.
The Role
Forward Deployed Data Engineer (security cleared)
We are hiring Forward Deployed Data Engineers across multiple levels (Junior through Senior Manager).
This is a hands‑on, client‑embedded engineering role. You will work close to operational users, helping them solve messy, ambiguous problems by designing and building robust data pipelines, workflows, and decision‑support tooling.
You are not just “building data platforms” in isolation; you are deploying engineering capability directly into client environments and adapting solutions to real constraints.
What You’ll Do
- Buddy up with clients: Work directly alongside stakeholders to understand operational challenges and translate them into technical solutions.
- Design under ambiguity: Break down poorly defined problem spaces and design pragmatic, scalable architectures.
- Engineer data pipelines: Build and maintain ETL pipelines that support operational and analytical use cases.
- Develop workflows: Create decision‑support tools and operational workflows that real users rely on.
- Apply AI where it counts: Collaborate on deploying AI and ML models that solve real problems, not experiments.
- Write production code: Deliver clean, reliable, maintainable software across the stack.
- Raise standards: Mentor peers, contribute to internal best practice, and help set the engineering bar.