CRM and Data Analyst

Stanford on Soar
3 months ago
Applications closed

Related Jobs

View all jobs

Marketing Data Analyst

Data Analyst

Data Analyst

Data Analyst

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

Data Analyst

CRM & Data Analyst – SaaS Sector
£45,000 - £50,000 per annum • Hybrid (3 days in modern Loughborough offices)
Benefits:

  • Unlimited holiday
  • Volunteer Day to work in their local community
  • Perk Box
  • Pension Contributions
  • Company Events
  • Free Office Parking
    We’re partnering with a growing SaaS company in Loughborough to recruit a CRM & Data Analyst—a pivotal role for someone who loves making systems work smarter, data cleaner, and teams more effective. If you’re motivated by improving processes step by step and want to make a real impact on how a business uses HubSpot, this could be the ideal next move.
    The Role
    Acting as the steward of the company’s HubSpot environment, you’ll ensure data is accurate, structured and genuinely useful. You’ll work across the business translating operational needs into CRM improvements, clear reporting and actionable insights.
    This is a hands-on, collaborative role where you’ll help shape how HubSpot is used and champion best practices to support smarter decisions.
    Key Responsibilities
  • Own the structure of HubSpot: fields, pipelines, properties and workflows.
  • Maintain data standards, including regular quality checks and fixes.
  • Collaborate with teams to ensure data is captured consistently and effectively.
  • Translate business requirements into CRM updates, reports or dashboards.
  • Build and maintain dashboards to identify trends and highlight process gaps.
  • Recommend improvements to processes relying on HubSpot data.
  • Manage user permissions and support GDPR compliance.
  • Deliver short training sessions and guidance to help colleagues use HubSpot confidently.
    About You
    We’re looking for someone who enjoys working with data and understands how systems support day-to-day operations. You’ll be curious, organised and comfortable speaking with different teams. A natural problem-solver, you’ll be confident improving processes in a practical, incremental way.
    What’s on Offer
  • £45,000 salary + excellent benefits
  • Hybrid working—3 days a week in modern, collaborative Loughborough offices
  • Chance to shape the CRM function in a growing SaaS environment
  • Supportive, forward-thinking team environment
    If you’re ready to take ownership of a core business system and help elevate how a SaaS company leverages data, we’d love to hear from you.
    Apply now or get in touch with John Boggis for a confidential chat.
    Your Recruiters Limited are an equal opportunity employer, celebrating diversity and committed to creating an inclusive environment for all employees. All applicants will receive a response, with detailed feedback provided to those unsuccessful at the interview stage

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.