BI (Business Intelligence) Engineer

WEG Tech

Coventry

2 months ago

Applications closed

Related Jobs

View all jobs

BI & Data Warehouse Developer

BI/Data Analyst – Central London (Hybrid) - £55,000

BI and Data Engineering Lead

Data Analyst

Senior Data Analyst

Business Intelligence Developer

BI Engineers play a crucial role in designing, developing, and maintaining our data visualization and reporting solutions using Microsoft Power BI. They collaborate with various stakeholders across the university to gather requirements and transform complex data into intuitive and actionable visualizations. Their expertise in business intelligence will contribute to enhancing data-driven decision-making processes across the university.
Duties and responsibilities

Develop and maintain interactive and visually appealing reports, dashboards, and analytical solutions using Power BI.
Collaborate with stakeholders to gather requirements, understand data sources, and translate business needs into effective visualizations.
Supporting data literacy, promoting data product adoption and ensuring data governance policies are followed.

Optimize and improve existing Power BI solutions, ensuring data quality, performance, and usability.
Conduct training sessions and provide technical support to end-users, empowering them to leverage Power BI capabilities effectively.
Work closely with the IT team to ensure the integrity, security, and governance of data assets in compliance with university policies and regulations.
Participate in cross-functional projects and initiatives, contributing your Power BI expertise to enable data-driven solutions and support strategic objectives.
Collaborate with University stakeholders to identify key metrics, trends, and insights to drive informed decision-making.
Foster a culture of data-driven decision-making by promoting the value and benefits of Power BI across the university community.
Stay up to date with the latest Power BI features, functionalities, and best practices, and provide guidance on their application within the university's data ecosystem.
Essential Criterion
Degree in Computer Science, Information Systems, or a related field, or equivalent experience
Proven experience as a Power BI Developer or similar role, including designing and implementing end-to-end Power BI solutions.
Proficiency in using Power BI Desktop, and DAX formulas to develop interactive reports and dashboards.
Strong understanding of data visualization best practices.
Proficient in SQL for data manipulation and analysis.
Familiarity with data warehousing concepts and multidimensional data models.
Excellent problem-solving and analytical skills, with the ability to translate complex business requirements into user-friendly visualizations.
Strong communication and interpersonal skills to collaborate effectively with stakeholders at all levels.
Ability to work independently and manage multiple priorities in a dynamic environment.
Desirable Criterion
Masters degree in related subject
Knowledge of data governance and security principles, including GDPR compliance

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Feb 10, 2026

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.

Feb 3, 2026

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.

Jan 23, 2026

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.