Sales Operations & CRM System Lead

Milton Keynes
9 months ago
Applications closed

Related Jobs

View all jobs

Data Analytics Engineer (Microsoft Fabric)

Data Analyst Specialist Apprentice

TikTok Shop UK - Strategy & Operations Data Analyst

Data Analytics Manager - Heartwood Collection

Business Intelligence Developer

Business Intelligence Developer

Sales Operations & CRM System Lead

Department: Sales HQ
Type of Position: Permanent
Location: UK
Reporting to: Sales HQ Manager 

About The Company
Imagine being part of a Global leader, where innovation and customer satisfaction drive everything we do. Join our Sales HQ team for an exciting new challenge.
Primary Objective: Increase sales productivity, effectiveness, and revenue.

The Role:
Join our client as Sales Operations & CRM System Lead; overseeing and optimising our CRM, providing valuable insights and analysis to help enhance the productivity and efficiency of our sales teams. You'll own and maintain data integrity and be responsible for mapping our sales processes.

Key Responsibilities:

CRM Management: Oversee and maintain, the development and utilisation of the CRM across the company. Ensuring data accuracy, correct coding, and usability.
Reporting & Analytics: Managing the development and maintenance of our BI reporting. Using this platform to provide insights to support decision-making.
Forecasting & Budgeting: Managing sales forecasting and budgeting processes.
Training and Support: Provide training and the supporting material to the sales teams on tools and processes.
Performance Metrics: Using objectives and key results to measure effectiveness of sales operations initiatives.
Management insights: provide insights on customer behaviour product sales gaps and pricing anomalies. 

Essential skills, education, and experience:

Degree or HNC/D in Business Administration, information management or related field.
Experience in Sales Operations, Sales data analysis or similar.
Experience with producing power BI data and reporting dashboards.
Proficiency with CRM systems and sales analysis.
Strong communication and presentation skills.
Basic/ intermediate commercial understanding of business.
Ability to work independently and manage time.
Willingness to learn about the business
Ability to meet deadlines.
Versatile attitude and a team player. 

Desirable skills, competencies, and experience:

Other qualifications to support Information Systems or Data analysis
Sugar CRM experience and data management including validation, data matching and quality control
Versatile attitude to take on different tasks.
Show a will to succeed and make things happen.
Be a willing part of a Team which has diverse responsibilities within the business
What We Offer:
We are continuously working to make our team even more diverse and inclusive. We welcome applications from all and are committed to attracting, recruiting and retaining the most talented individuals. They have sustainable development goals as we believe in caring for our futures as well as the future of the planet.

We offer a competitive and attractive package of benefits including, retail discounts, life assurance, Private Medical Cover, 25 days holiday including a holiday purchase scheme, a salary sacrifice personal pension plan and more!

The Selection Process: Upon successful application, candidates will be asked to undertake a first interview online followed by some online testing and lastly a second interview face-to-face, onsite at our clients Offices

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.