Data Engineering Consultant

Harnham
London
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineering Consultant

Lead Data Engineering Consultant CGEMJP00330718

Lead Data Engineering Consultant CGEMJP

Senior Data Engineering Consultant

Lead Data Engineer

Lead Data Engineer

Data Engineering Consultant

Salary: £45,000 - £55,000

Location: London (4 Days a Week in Office)

Work on the front lines of innovation, embedding next-gen platforms directly into the operations of world-class organisations. Your engineering will become the competitive edge others can't replicate.

The Opportunity

This role offers the chance to work at the frontier of technology and real-world operations, embedding advanced data and AI systems directly into major industrial organisations. You'll operate as a problem-solver and builder, transforming complex business challenges into scalable, high-impact solutions. The position sits within a rapidly growing, category-defining startup where engineering excellence directly drives customer success.

You'll collaborate closely with an elite, interdisciplinary team while gaining exposure to cutting-edge platforms, generative AI workflows, and large-scale data integration. It's an opportunity to shape transformative systems that deliver meaningful, measurable outcomes across global enterprises.

Role and Responsibilities

This role centers on working directly with customers to deploy and tailor a powerful data and AI platform to solve their most complex operational challenges. You'll design and implement scalable generative AI workflows, often using technologies like Palantir AIP, while building robust data pipelines with PySpark, Python, and SQL. A key responsibility is executing sophisticated data integrations across distributed systems and enterprise environments, including ERPs and CRMs.

You'll collaborate closely with client stakeholders to translate ambiguous requirements into clean, maintainable solutions that drive real impact. Familiarity with tools such as Airflow, DBT, Databricks, dashboarding frameworks, and Typescript is a strong plus as you help deliver end-to-end production-ready systems.

Interview Process

  1. Teams conversation (introductory chat)
  2. Technical take home exercise
  3. Presentation (In-Person)
  4. Conversation with the CTO

Step into a role where your engineering drives real-world impact and shape the future of enterprise technology-apply today.

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.