Data Engineer - Stevenage- Hybrid- Up to £70K

Stevenage
3 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer - Stevenage- Hybrid- Up to £70K

We are looking for an experienced Data Engineer to join a high-profile programme delivering one of the largest data migration projects of its kind in the UK. This is a unique opportunity to work on a nationally significant initiative that will transform how data is managed and accessed across the several organisations.

The successful candidate must be eligible for security clearance due to the sensitive nature of the project.

In this role, you will analyse existing data structures and design robust migration strategies to move complex datasets to cloud environments. You will develop scripts and automated processes for data extraction, transformation, and loading (ETL), ensuring data quality and integrity throughout the migration process. Working closely with business users and IT teams, you will plan, coordinate, and execute migrations within strict timelines, troubleshoot issues promptly, and align processes with organisational goals and regulatory standards.

You will bring expert-level SQL skills for complex query development, performance tuning, and indexing strategies, enabling accurate and efficient migration from on-premises databases such as SQL Server, Oracle, MySQL, and NoSQL to AWS cloud. Strong knowledge of ETL processes is essential, including experience with tools such as Talend, Informatica, Matillion, Pentaho, MuleSoft, Boomi, or scripting languages like Python, PySpark, and SQL. A solid understanding of data warehousing and modelling techniques, including Star and Snowflake schemas, is required. Ideally you will also have a comprehensive knowledge of AWS glue.

To succeed, you will demonstrate proven experience in data engineering, data migration, and ETL development, as well as strong analytical skills to assess data quality, resolve inconsistencies, and manage end-to-end migration projects. Your ability to collaborate with technical and non-technical stakeholders, ensure compliance, and deliver projects on time will be key to your success.

In return receive £70,000 depending on experience as well as an excellent benefits package and 3 days working from their modern office based in Stevenage.

If you are passionate about data, thrive in complex migration environments, and want to contribute to one of the most significant projects in the country, we would love to hear from you. Apply now by sending your CV to

Modis International Ltd acts as an employment agency for permanent recruitment and an employment business for the supply of temporary workers in the UK. Modis Europe Ltd provide a variety of international solutions that connect clients to the best talent in the world. For all positions based in Switzerland, Modis Europe Ltd works with its licensed Swiss partner Accurity GmbH to ensure that candidate applications are handled in accordance with Swiss law.

Both Modis International Ltd and Modis Europe Ltd are Equal Opportunities Employers.

By applying for this role your details will be submitted to Modis International Ltd and/ or Modis Europe Ltd. Our Candidate Privacy Information Statement which explains how we will use your information is available on the Modis website

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.