Data Analyst/Administrator Full-Time Starting Salary £28,000 Per Annum

cks-productions | TONSTUDIO & AUDIOAGENTUR
Manchester
1 month ago
Applications closed

Related Jobs

View all jobs

HRIS Administrator and Data Analyst

HRIS Administrator and Data Analyst

Data Analyst Training Course (Excel, SQL & Power BI)

Trainee Data Analyst - Training Course

Trainee Data Analyst - Training Course

Trainee Data Analyst

Data Analyst/Administrator Full‑Time Starting Salary £28,000 Per Annum

Are you self‑motivated and pro‑active? Proficient with Microsoft packages? Analytical mind? Worked with a variety of data systems? If you answered yes, this may be the next role for you. This opportunity is within our contract administration team, providing operational assistance to the Network Rail contract. The role will work closely with clients and operational teams to ensure great customer service and monitor performance.


What You’ll Do

  • Support the Contract Management Team in day‑to‑day functions of EV operations and overall administration, including analysing EV and ANPR camera data.
  • Promote operational excellence and best practice, forming close partnerships with internal and external stakeholders.
  • Ensure operating procedures comply with both APCOA Parking UK and Network Rail.
  • Liaise with Network Rail, its third‑party partners, and internal APCOA stakeholders regarding all EV matters.
  • Advise line manager/Contract Manager on cost efficiencies and support generation of additional revenue across the contract.
  • Advise on operational challenges impacting EV performance and propose solutions.
  • Manage new site mobilisations and setups for EV charging.
  • Monitor EV Point and ANPR performance across the Network Rail parking estate, including fault reporting and resolution.
  • Support the contract and contribute to delivering all client and APCOA local SLA and KPIs.
  • Provide additional support to operations teams, including site visits, health and safety inspections, and ad‑hoc enforcement.

What You’ll Bring

  • Previous experience within an operational role.
  • Experience working with multiple stakeholders.
  • Experience with reporting and cost controls/budgets.

Do you think you could be the right person for this role? If you have a passion for excellence, a knack for managing contracts, and a drive to elevate customer experiences, we want to hear from you. APPLY NOW!


We are committed to ensuring APCOA is a fair place to work regardless of age, race, gender, sexuality or level in the organization. We offer a motivating environment where successes are shared and individuals have development opportunities to fulfil their potential while aiming for excellence.


#J-18808-Ljbffr

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.