Solutions Manager - London - Hybrid - £50K

London
9 months ago
Applications closed

Related Jobs

View all jobs

Manager, Data Science

Senior Business Intelligence Analyst / Data Manager

Manager Quantitative Analysis - Centre for UK Growth

Audit Data Analytics Manager

Business Intelligence Consultant - Power BI & SQL

Digital Data Consultant, Data Engineering, Data Bricks, Part Remote

Ada Meher is currently resourcing for a Solutions Manager to join an innovative property technology company as they roll out their next-generation property management platform across the student accommodation and build-to-rent sectors.

You’ll play a key role in ensuring successful onboarding and implementation for new clients, taking ownership of system configuration, process improvement, and client training. The successful candidate will act as a crucial bridge between technical teams, end users, and senior stakeholders, helping to streamline operations and elevate customer experience through digitisation and automation.

This is an ideal opportunity for someone who thrives in dynamic environments, enjoys cross-functional collaboration, and is passionate about how technology can transform traditional industries.

Key Responsibilities:

•             Lead client onboarding and implementation processes for the platform
•             Act as the internal subject-matter expert on system functionality and usage
•             Support testing, release planning, and rollout of new features and upgrades
•             Develop training materials and documentation for users at all levels
•             Analyse client requirements, identify solutions, and support delivery
•             Provide support across enterprise systems including CRM, finance, and reporting tools
•             Manage data quality and reporting using tools such as SQL, Tableau, HubSpot, and Excel
•             Coordinate with stakeholders across operations, product, and vendor teams
•             Run post-implementation reviews and contribute to future improvements
•             Contribute to ongoing process optimisation and digitisation efforts

To Be Considered:

•             Proven experience in a Solutions, Systems, or Business Analyst role within a tech-driven organisation
•             Hands-on experience with CRM, BI, or finance systems (e.g. HubSpot, Tableau, Qube, Sage)
•             Strong data analysis and reporting skills, with experience using SQL and Excel
•             Excellent communication skills and the ability to translate technical ideas for non-technical audiences
•             Experience managing projects or initiatives from planning through to delivery
•             Demonstrable stakeholder management and documentation skills
•             Ability to thrive in a fast-paced, collaborative environment

Desirable Skills:

•             Familiarity with property management platforms (MRI Qube, Yardi, etc.)
•             Experience with data migration, ETL tools, and system integration
•             Technical skills in scripting or programming languages (e.g. Python, Ruby)
•             Experience with Microsoft SQL and business process mapping (UML)

This company is at the forefront of digital transformation in the property sector, offering an exciting opportunity to shape how operational technology impacts tenants, landlords, and property managers alike. With flexible working, a collaborative team culture, and significant ownership over your work, this is an ideal role for a tech-savvy problem-solver ready to make an impact.

We are looking to hire for this role in the coming weeks and shortlisting has already begun. For consideration, please apply immediately or get in touch with Chaz or Matthew via email:
(url removed)
(url removed)

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.