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

APCOA PARKING UK
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, then this may be the next role for you! Keep reading to find out more…


An exciting opportunity has arisen within our contract administration team to provide operational assistance to the Network Rail contract. This role will work closely with our clients and operational teams ensuring great customer service and monitoring performance of this area.


What you’ll do:

  • To support the Contract Management Team in the day-to-day functions of EV operations and overall administration including analysing EV and ANPR camera data
  • Promote operational excellence and best practice and form a close working partnership with internal and external stakeholders.
  • To ensure operating procedures comply with both APCOA Parking UK and Network Rail.
  • To liaise with Network Rail (and its third-party partners) and internal APCOA stakeholders in regard to all things EV
  • To advise line manager/ Contract Manager of any cost efficiencies and support generation of additional revenue gain across the contract
  • To advise line manager of any operational challenges which impact EV performance and how they can be overcome
  • To manage new site mobilisations and setups in regard to EV charging
  • To manage and monitor the EV Point and ANPR performance across the Network Rail parking estate including end-to-end management of fault reporting and the resolution of them
  • To support the contract and ultimately contribute to delivering all client and APCOA local SLA and KPIs.
  • The role will also include additional support for the operations teams to include site visits, health and safety inspections and ad-hoc enforcement

What you’ll bring:

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

Do you think you could be the right person for this role? Is this the next opportunity you are looking for?

If you have a passion for excellence, a knack for managing contracts, and a drive to elevate customer experiences, this is your ticket to an exciting journey and we want to hear from you.


APPLY NOW!


We are focused on ensuring APCOA is a fair place to work regardless of age, race, gender, sexuality or level in the organisation. We offer a motivating work environment where successes are shared. With challenging projects and an atmosphere of fostering and support, staff have the development opportunities to fulfil their potential while aiming for excellence in their work.


#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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.