Senior Data Engineer

Anson McCade
London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

This range is provided by Anson McCade. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.


Base pay range

A leading AI and technology consultancy is looking for Senior Data Engineers to join their fast-growing team. This is a unique opportunity to apply your technical expertise in AI, advanced analytics, and data engineering while shaping how organisations make decisions at scale.


About the Role

This is a hands-on, client-facing position where you’ll lead technical delivery and act as a trusted advisor. You’ll design and implement innovative solutions, build complex data pipelines, and integrate AI models into operational workflows across industries. The role offers a rare chance to combine software engineering mastery with strategic problem-solving, directly impacting government, defence, healthcare, and commercial clients.


What You’ll Do

  • Lead and architect scalable solutions using cutting-edge data platforms.
  • Engineer enterprise-level data pipelines and optimise ETL processes.
  • Develop workflows and decision-support tools that transform operations.
  • Integrate AI and machine learning into client systems, ensuring measurable outcomes.
  • Partner with senior stakeholders to translate business needs into technical solutions.
  • Set engineering standards, champion best practices, and drive continuous improvement.
  • Mentor junior engineers and contribute to a culture of innovation and collaboration.

What We’re Looking For

  • 5+ years in software or data engineering, with strong Python, SQL, and TypeScript skills.
  • Experience with enterprise data platforms, ideally Palantir technologies (Foundry, Gotham, or similar).
  • Advanced knowledge of data engineering, ETL pipelines, and workflow design at scale.
  • Proven track record of leading projects and mentoring teams in client-facing environments.
  • Passion for AI, machine learning, and translating emerging technologies into real-world impact.
  • Exceptional problem-solving, communication, and stakeholder management skills.
  • Curiosity, adaptability, and a drive to make a tangible difference.

Why You’ll Join

  • Work at a fast-growing AI consultancy backed by a global technology leader.
  • Lead mission-critical projects that influence government, healthcare, defence, and commercial sectors.
  • Gain hands-on experience with advanced analytics, AI, and enterprise data platforms.
  • Build your career story as a senior leader in data-driven transformation.
  • Thrive in a collaborative, innovative culture that values curiosity and bold thinking.

If you’re ready to engineer the future of AI-powered decision-making and make a real-world impact, we want to hear from you.


Seniority level

  • Mid-Senior level

Employment type

  • Full-time

Job function

  • Consulting and Information Technology

Industries

  • IT Services and IT Consulting and Technology, Information and Media

AMC/AQI/D-SDE


#J-18808-Ljbffr

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.

Careers

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.

Jobs

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.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.