Revenue Data Analyst - Maternity Cover

Celtic Manor Resort
Newport
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Analyst

Business / Data Analyst

Marketing Data Analyst

Data Analyst - Data Quality & CRM Migration - £40k

Data Analyst - Data Quality & CRM Migration - £45k

Data Analyst - Data Quality & CRM Migration - £40k

JOB TITLE : Revenue DataAnalyst


DEPARTMENT : Sales and Revenue


CONTRACT TYPE : Temporary – Maternity Cover


RATE OF PAY : £ per hour + excellent benefits


HOURS : hours over 5 days


LOCATION : Newport, NP18 1HQ


ID : REQ5918


Are You a Data Enthusiast with a Passion for Hospitality?


Join the award-winning Celtic Manor Resort and become a key player in our Sales and Revenue team. We’re looking for a Revenue Data Analyst who thrives on uncovering insights, spotting trends, and turning numbers into strategy. If you love data and want to be part of a dynamic, fast-paced environment where your analytical skills make a real difference - this is the role for you.


Why You’ll Love This Role

Be at the heart of revenue strategy for one of the UK’s leading hospitality brands.


Work with cutting-edge systems like Opera Cloud, Delphi, Ideas, and more.


Dive deep into data to influence pricing, booking trends, and revenue growth.


Collaborate with a passionate team and contribute ideas that drive performance.


What We’re Looking For

  • Strong Excel skills and experience in report writing.
  • Familiarity with hospitality systems (Opera Cloud, Delphi, Lanyon RFP, GDS).
  • A natural curiosity for data and trends.
  • Confidence in making data-driven decisions quickly and accurately.
  • Previous experience in a similar role is a big plus!

Your Day-to-Day Will Include

  • Creating and analysing reports for strategy meetings.
  • Monitoring booking patterns and identifying opportunities.
  • Managing rate codes and yield strategies in Opera Cloud.
  • Collaborating with third-party partners to boost sales.
  • Bringing fresh ideas to increase revenue and average rates.

Perks & Benefits

  • Career development from day one.
  • Discounts on food, drinks, and hotel stays.
  • Discounted leisure membership.
  • Free virtual GP appointments.
  • 24 / 7 wellbeing support.
  • NEST pension scheme.
  • Social events and staff appreciation days.
  • Monthly and annual awards.

Grow With Us

At The Celtic Collection, we believe in empowering our team. You’ll be supported, trained, and recognised for your contributions. We foster a culture of inclusion, innovation, and growth—where your ideas matter and your development is a priority.


Ready to Crunch the Numbers and Shape the Future of Hospitality?

Apply now and become one of our Hospitality Heroes.


Help us write the next chapter in the Greatest Story in Hospitality.


#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.