SC Cleared - Data Engineer - Python, SQL

London
11 months ago
Applications closed

Related Jobs

View all jobs

SC Cleared Data Analyst

Oracle Data Warehouse Developer (SC Cleared)

Data Analyst - Sc cleared

Data Architect (SC Cleared)

Data Analyst (HR Data) – SC Cleared

Data Analyst - Aerospace

Role: Consultant data engineer (SC cleared)

Location: London

Salary: £50,000 - £65,000

Remote work: there is remote work, but you are required to be in the office approx 2 days per week wither Farringdon OR Westminster

** You MUST be SC cleared or at least eligible with a UK passport""

Key Skills Required:

Data Engineering, Python, SQL, Virtualisation, TypeScript. They don't need to have used Palantir tech, but is a bonus if they have
Need SC Clearance (will consider people who have had it, where is has lapsed)

Role description:

·

Consultant Engineer specialising in Palantir software and delivering strategic advisory, engineering delivery, and enablement services to government and commercial clients.

·

This role seeks to solve user problems by drawing source and captured data into a single foundational data model and building operational workflows that materially change the decisions made within an enterprise for the better.

·

The role brings rapid and sustainable value to clients utilising Palantir technology, enabling them to create greater benefit from their technology, and working closely with clients to support them in the underpinning change and improvement requirements.

·

you will be part of the journey of building a rapidly growing company with strong ideals and ambitions. You will work closely with teams of experienced and early talent engineers on a variety of projects, gaining hands-on experience in data management, analysis, and AI technologies, on profound and important problems.

Key Responsibilities:

·

Decomp and design solutions for client problem sets based on their descriptions and by exploration of the data landscape.

·

Assist in the design, development, and maintenance of data pipelines and ETL processes to build data and action models to address the workflow needs.

·

Build and edit operational workflows, including front ends and decision-support toolsets, inclusive of native Palantir tooling and integrated front ends.

·

Collaborate with team members to implement AI and machine learning models against user problems.

·

Engage in continuous learning and development on a personal basis and contribute to the core technical and best practice reachback function.

This may include upskilling peers or junior engineers.

·

Close working with clients, building deep and sustainable relationships that positions you/us as their "trusted advisor"

How to apply?

Please send your CV to

People Source Consulting Ltd is acting as an Employment Agency in relation to this vacancy. People Source specialise in technology recruitment across niche markets including Information Technology, Digital TV, Digital Marketing, Project and Programme Management, SAP, Digital and Consumer Electronics, Air Traffic Management, Management Consultancy, Business Intelligence, Manufacturing, Telecoms, Public Sector, Healthcare, Finance and Oil & Gas

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.