Date Analyst - Metering

Bury
11 months ago
Applications closed

Related Jobs

View all jobs

Sales Data Analyst TLNT1_NI

Sales Data Analyst

Data Analyst

Data Analyst

Senior Data Analyst

Senior Data Analyst

Job Title: Data Analyst

Location: Bury - on site

Level / Salary Range: Up to £27,000

Role Overview:

Time Recruitment is representing a client seeking a motivated and detail-oriented Data Analyst to join their growing team. The primary focus of this role is to support the management of a metering portfolio, liaise with stakeholders, and ensure accurate data flows related to Metre Asset Managers (MAMs). The successful candidate will play a key role in ensuring the timely and efficient resolution of queries and managing metering processes within the energy industry.

Key Responsibilities:

Manage all Metre/Automatic Metre Reader (AMR) installations, exchanges, removals, and asset update data flows, ensuring data sources are aligned and maintained.
Source key stakeholder details and update all relevant systems, ensuring all appointments and de-appointments are correct.
Resolve issues arising from incorrect metre or AMR data promptly and efficiently.
Manage and rectify industry metre reading rejections.
Liaise with customers, metering partners, reading agencies, other energy suppliers, and internal stakeholders to resolve metre and data queries.
Support the business in its AMR and Smart metre roll-out strategy.
Request, remove, or re-synchronise AMR devices with metering agencies within agreed SLAs.
Ensure metre readings are obtained and submitted within specified timeframes.
Process industry file flows that update metre point data, ensuring accurate billing at both industry and supplier levels.
Maintain and develop high levels of customer service to support the operational and sales functions of the business.
Provide regular and ad hoc reports.

Desired Personal Attributes:

Strong verbal and written communication skills.
Excellent organisational abilities and attention to detail.
Ability to prioritise and manage tasks in a fast-paced environment.
High level of accuracy in all areas of work.
Initiative to propose solutions and take action independently, with the confidence to challenge the status quo.
Flexible approach, with a willingness to assist in other areas of the business.
Excellent interpersonal skills and the ability to build relationships with senior managers and stakeholders.
Strong persuasion, influencing, and negotiation skills.

Advantageous Skills:

Proficient in MS Office, particularly MS Excel.
Experience in an operations function within an energy supplier.
Advanced MS Excel knowledge.
Experience working as a third-party agent such as a MAM/MOP/DC.
This role offers an excellent opportunity to join a dynamic, growing team. The duties listed above are not exhaustive and may evolve in line with the business's needs.

Benefits:

Free breakfast and lunch in the office
25 days holiday (plus bank holidays)
Annual bonus scheme
Flextime
Free parking
Wellbeing support
Onsite gym
Exciting social and team-building events
To find out more about this exciting opportunity, apply today

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.