Business Intelligence Developer

PIB Group
Lincoln
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Job purpose: To work with a wide range of business datasets, creating and analysing key business performance indicators in a dynamic and constantly changing B2B & B2C environment.  Key Responsibilities: Develop new Business Intelligence across a variety of business functions through a process of requirements gathering, designing, building, testing and releasing for each new report and application.  Develop new databases in Microsoft SQL and deliver reports using SSRS, Microsoft Power BI and Salesforce as required by the business.  Develop data models using Microsoft Analysis Services (SSAS) for consumption within Microsoft Power BI, Excel or SSRS.  Complete data mining identifying business trends within datasets using data models developed in SSAS.  To ensure accuracy within all reporting by testing outputs using the business tools and resources available.  To be able to articulate to a variety of different business stakeholders.  Create technical documentation for BI tools.  Regularly review live reporting and database applications in an order that outputs remain aligned to business and user needs.  Provide advice, guidance and coaching to team members to enhance their technical capabilities and ensure value added service to customers.  Manage and evolve the day-to-day relationships with internal and external partners. Help support Salesforce developing and assisting the salesforce analyst(s) to help enact positive change for the B2B sales team. Understands all aspects of Salesforce configuration and technical/functional capabilities, including all changes and potential system implications related to the Salesforce release upgrades (currently scheduled 2-3 times a year) Maintaining the current Salesforce data integration using the Synatic platform. Aid in the execution of opportunities to enhance Salesforce solution, driving better functionality for internal and external customers, including external reporting for agent customers. REF-(Apply online only)

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 to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.