Senior Manager Enterprise Data Transformation

Costa Coffee
St Albans
1 month ago
Applications closed

Related Jobs

View all jobs

Data Analyst Senior Consultant, Assistant Manager, Manager - Belfast

Senior Data Engineer - HUGE DATA LAKE! North Lincolnshire- £55K

Data Quality Improvement Manager

Data Engineer

Senior Data Engineer

Data Governance Analyst, Data Owner, Data Business Analyst,City London

Senior Manager Enterprise Data Transformation

Join Costa Coffee, a key part of the Coca‑Cola System, where we reimagine coffee experiences in over 50 countries. From in‑store service to on‑the‑go options, we deliver great moments for every customer.


As a Senior Manager Enterprise Data Transformation, you will shape and drive our data strategy, leading transformative projects and elevating the company’s enterprise data ecosystem.


Why Costa

We are a global brand that values passion, progression, and integrity. Our culture is defined by "Disciplined to Deliver, Passion for Progress, Win with Warmth, Courage to Challenge, and Trusted Team Player."


Benefits

  • Share Investment Plan – share ownership in Coca‑Cola
  • Smart pension with tax and national insurance savings, matching contributions up to 10%
  • Financial Support Fund for unexpected financial pressure
  • 50% discount in all Costa‑owned stores, 25% off in participating stores
  • Private medical Cover via Private Healthcare Scheme
  • Explore additional perks here: https://bit.ly/costaper

Responsibilities

  • Lead and orchestrate the data ecosystem for Enterprise Transformation projects, aligning architecture with business outcomes.
  • Manage integration of disparate data sources, oversee data modelling and transformation, and enable reporting for stakeholders.
  • Own and manage ERP data from multiple D365 systems within the enterprise platform, delivering and evolving Power BI solutions.
  • Partner with technology, business, and finance teams to understand requirements, prioritise initiatives, and ensure robust delivery.
  • Stay ahead of industry trends and emerging technologies, bringing new ideas that provide competitive advantage.

Qualifications

  • Extensive experience in enterprise data, transformation programmes, and data architecture.
  • Strong understanding of D365 data structures with expertise in modelling, integration, and reporting platforms.
  • Hands‑on experience with Databricks, Power BI, Power Query, DAX, tabular model design, star and snowflake schema design.
  • Track record of delivering end‑to‑end data solutions for complex business initiatives.
  • Excellent stakeholder management and communication skills, leading analytics initiatives from scoping to deployment.

Where you’ll work

Support Centre teams work flexibly, blending home working with in‑person time as needed. The new brand headquarters in St Albans (January 2027) will support three days a week of in‑person collaboration.


Seniority Level

Mid‑Senior level


Employment Type

Full‑time


Job Function

Information Technology (Retail, Technology, Media, Food and Beverage Services)


Referrals increase your chances of interviewing at Costa Coffee by 2x.


Get notified about new Data Manager jobs in St Albans, England, United Kingdom.


#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.