Quantexa Data Engineer

Birmingham
3 months ago
Applications closed

We are looking for a permanent Data Engineer to help implement Quantexa for a global brand based in The Midlands. Hybrid, up to £80k base + benefits.

ABOUT THE ROLE

We are looking for an experienced Senior Data Engineer with Quantexa expertise to join our growing team, which forms part of a larger, successful organisation working in partnership with a key government client. You will play a pivotal role in shaping and delivering entity resolution solutions for our clients and helping us expand our capability in Quantexa's wider Decision Intelligence platform.
This is an exciting opportunity to be at the forefront of emerging demand for Quantexa-based services, leading engagements, influencing solution design, and developing capability across our teams.

YOUR RESPONSIBILITIES

  • Lead the design and implementation of enterprise solutions using Quantexa.
  • Collaborate with product teams and client stakeholders to define matching logic and ensure high quality data inputs.
  • Build and maintain data pipelines feeding into matching and analytics services.
  • Conduct data profiling and analysis to ensure high-quality inputs.
  • Optimise matching algorithms for performance and accuracy.
  • Support incident resolution and ensure service continuity.
  • Share knowledge and coach colleagues to grow Quantexa capability.
  • Actively participate in Agile ceremonies and work cross-functionally with engineers, analysts and business teams.

    WHAT YOU'LL BRING

    Essential Skills and Experience:

  • Hands-on experience with the Quantexa platform, particularly entity resolution.
  • Strong data engineering background, including data profiling and integration.
  • Familiarity with APIs for data access and integration.
  • Excellent client-facing and consultancy skills.
  • Experience working in Agile delivery environments.
  • Drive to share knowledge, mentoring and developing others.
  • Active SC Clearance, or eligibility to obtain

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.

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.

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.