Senior Data Analyst

Dennistoun
3 days ago
Create job alert

Your New Role

We have an exciting opportunity for a Senior Data Analyst to join our Transport Infrastructure team based in Polmadie, Glasgow. This is a full-time, permanent position offering hybrid working (Polmadie, Glasgow Office) and a 37.5-hour week, Monday to Friday.

As a Senior Data Analyst, you'll play a key role in providing high-quality data and insight to contract and divisional management teams, helping drive performance across our Scotland and Northern Ireland accounts.

You will be responsible for:

Supporting the creation of training materials and helping operational managers use reporting tools and dashboards effectively.
Producing performance and exception reports, including daily/weekly/monthly reporting aligned to contract targets.
Conducting dynamic planning and developing reporting that improves logistics, planning and overall operational efficiency.
Working with contract and digital teams to drive ongoing improvements and develop agreed objectives.
Identifying and liaising with local data owners to ensure accurate, timely and complete data capture across systems.
Ensuring all performance data is validated, formatted correctly and submitted on time to required stakeholders.
Maintaining standardised data formats compatible with the data lake and ensuring system integrity.
Creating analytical reports combining sources such as Masternaut and timesheets to demonstrate productivity.
Responding to ad hoc requests for performance-related data and insight.
Analysing KPIs to identify trends, issues and improvement opportunities, including benchmarking against similar contracts and industry standards.
Collaborating with Group IT to enhance and automate data collection processes.
Challenging data contributors where required to ensure accuracy and reliability.
Preparing clear, insightful reports for senior managers with recommended actions.
Capturing and evaluating innovations and ideas from the business and promoting a culture of fact-based decision-making, constructive challenge and collaboration. We want to hear from you if you have:
Strong desire to support operational teams and improve efficiency across the contract
Advanced Excel skills and confidence working with data
Ability to use Microsoft Power Platform to create automations and dashboard reporting
Excellent communication and presentation skills, able to explain complex data clearly
Strong analytical, organisational, and planning abilities
Willingness to embrace new technologies, including AI, to enhance processes
Ability to challenge data accuracy and review existing processes constructively
Demonstrates creativity, innovation, and a continuous improvement mindset
Able to work collaboratively with operational teams and influence decision-makers through data-driven insights
Capable of working independently and as part of a team to design and implement reporting solutions
Maintains strong awareness of Health & Safety requirements
Knowledge of Highways Maintenance (advantageous) In addition to this, it would be essential if you a relevant academic development or experience in a similar data or performance-focused role

What we offer you

When you join us, we can offer flexibility, career development, a choice of benefits and support that help you through all life's ups and downs. It's the reason why Investors in People put us among the top 1% of employers and we have a competitive reward and benefits program.
Career Development - Exceptional development and progression plan
Pension - Generous Pension scheme which we will contribute to
Holidays - Minimum 24 days holiday + Bank Holidays
Choices - Our flexible benefits scheme is tailored by you, including buying additional annual leave, cycle2work scheme, charity giving and gym membership
Save with Amey - Our online voucher portal gives you access to thousands of discounts from leading retailers to help you save on shopping, days out, or nights in. It includes healthcare, free GP service, dental vouchers
Social Value - You'll get 2 Community Involvement Days each year to volunteer for a charity of your choice and further opportunities to support fundraising initiatives Application Guidance

At Amey, we value a culture of diversity and inclusion. We encourage applications from individuals who are passionate about making a positive impact, no matter their background, gender, race, or personal circumstances. We believe everyone deserves the opportunity to shine.

As a Disability Confident leader, we're proud to offer applicants with a disability an interview if they meet the minimum requirements for the role.

If you have any questions or need any adjustments during the recruitment process, don't hesitate to reach out to Susan Rutherford, our recruiter for this role, at (url removed) .

#CVL

#LI-SR1

Related Jobs

View all jobs

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

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.