Data Warehouse Engineer

St Albans
9 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Data Engineer

Senior Data Engineer

Data Engineer - Birmingham

Data Engineer

Senior Data Engineer

Job Title: Data Warehouse Engineer
Job Type: Permanent
Work Type: Hybrid
Industry: FMCG/IT
Job Location: St Albans
Salary: £70,000 to £90,000 per annum + 10% Bonus + Car Allowance + Private Medical

Profile – Data Warehouse Engineer

Our client is a leading figurehead in the UK food sector, with an unparalleled pedigree dating back to the early 18th century. They are currently seeking a Data Warehouse Manager to support their data and analytics department as the company continues to heavily invest and undertake a sustain period of IT transformation.

Job Role – Data Warehouse Engineer

Reporting to the Head of Insights and Intelligent Automation the Data Warehouse Engineer is responsible for driving the data and analytics strategy for the company. Ensuring alignment with business goals and objectives.

The Data Warehouse Engineer shall play a key role in shaping the data and analytics strategy and to contribute to driving business performance through effective data-driven decision-making.

Duties – Data Warehouse Engineer

• Lead the development and implementation of data and analytics solutions that drive business performance and decision-making.
• Work closely with the business partnering team to understand business requirements, design appropriate analytic solutions, and oversee the development and deployment of the solutions that support the requirements
• Deliver high-quality data and analytics solutions that meet business objectives.
• Work alongside a team of data professionals to foster a culture of continuous improvement and innovation.

Experience/Qualifications – Data Warehouse Engineer

• In depth experience with AWS Services (S3, Redshift, RDS)
• Proficiency in Matillion ETL
• Strong SQL skills
• Experience with data warehousing concepts and best practice
• Experience working in FMCG environment

Candidates who are currently a ETL Engineer, Data Architect, Data Warehouse Developer, Data Platform Engineer, Database Developer, Big Data Engineer and Data Warehouse Engineer could be suitable for this position.

To make an application for this role please submit your CV to (url removed) or for more information call (phone number removed).

For details of other opportunities available within your chose field please visit our website (url removed)

Omega is an employment agency specialising in opportunities at all levels within the Engineering, Manufacturing, Aerospace, Automotive, Electronics, Defence, Scientific, Energy & Renewables and Tech sectors

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.