Business Intelligence Developer

Harrington Starr
Sheffield
1 year ago
Applications closed

Related Jobs

View all jobs

Business Intelligence Developer

Business Intelligence Developer

Business Intelligence Developer

Business Intelligence Developer / Reporting Analyst

Business Intelligence Developer

Business Intelligence Developer

Business Intelligence Developer (x2)| Fintech Start-up (remote) | £50,000 - £60,000 + Benefits


My client is one of Europe’s Leading Financial Services Start-up who specialize in the payments space. Beyond this they have also been voted one of the top 20 technology companies in the whole of the UK.


As they continue to expand, we are now looking for 2 new BI Developers to join their team. You will work closely with cross functional teams including developers, data scientists, product managers, implementation teams and business stakeholders.


Apart from coming into the Northeast office once a quarter to meet with the team, this role can be conducted on a fully remote basis but you must be eligible to work in the UK.


What are we looking for?


  • Design, develop, and maintaining the company's business intelligence solutions using software such as Looker and Snowflake.
  • Experience of working with cloud based BI tools such as data visualisation tools Looker, Power BI, Tableau.
  • Exposure to cloud technologies and principles (AWS/Azure) of designing and developing data solutions on public cloud - AWS/Azure.
  • Establish data quality control mechanisms, including data validation, cleansing, and enrichment procedures, to maintain high data accuracy and reliability.
  • Proficiency in programming languages commonly used for data integration and data analysis, such as Python, R, Java and SQL.
  • Ensure compliance with data governance policies, data privacy regulations, and industry standards throughout the data integration process.
  • Prior experience working in Fintech/Payments would be a big plus.


Apply today or get in touch with me for more info at

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.