Data Analyst Utilities

Bury
11 months ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Due to constant growth, I am looking for an experienced Metering Data Analyst to join a very successful utility organisation in the Rochdale area. 

What are the responsibilities? 

The management of all Meter/Automatic Meter Reader (AMR) installations, exchanges, removal or asset update data flows ensuring all data sources are aligned and maintained
Sourcing key stakeholder details and updating all relevant systems and ensuring all appointments and de-appointments are correct
Resolving any issues that may arise from incorrect meter and/or AMR data in a timely and effective manner
To manage and remedy industry meter reading rejections
Liaise with customers, metering partners, reading agencies, other gas suppliers and internal stakeholders to resolve meter and data queries in a timely and effective manner
To support the business with its AMR and Smart meter roll out strategy
To request, remove or re-synchronize AMR devices with metering agencies within agreed SLAs
Ensure meter readings are obtained and submitted to the industry within a specified timeframe
Process industry file flows that update relevant meter points so that the supply point data held is accurate both at industry and supplier level to allow accurate billing
Maintain and develop high levels of customer services to support Crown Gas & Power’s operational and sales functions
Providing regular and adhoc reports

The perks:

On-site gym and shower
Free parking 
Great culture
Breakfast and lunch provided
Sporting events 
Social and interactive events
Please note, this is a full time role, Monday to Friday in the office. 

Thanks

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.