Managing Consultant - Technical Business Analyst (SC Eligible)

Stealth iT Consulting
Manchester
1 year ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Scientist

Data Quality and Systems Manager

Data Engineer (SC Cleared)

Data Analyst

My client is a global leading Digital Consultancy specialising in Cloud Adoption, Digital Architecture/Transformation & NextGen AI Solutions, with offices in London, Manchester and Glasgow providing a remote first policy + ad hoc client site visits when required (on average 1-2 visits per month, but can be more/less).


They are looking for a Management Consultant - Technical Business Analyst to join a rapidly growly team, working across multiple Digital Transformation projects within Government, Finance, Retail & Energy sectors and we have a top end budget of £88000 + £8k disc bonus + full bens available.


Primary skills and experience required:

  • Must be eligible or already possess SC Clearance
  • Proven experience managing a team of Business Analysts
  • Experience in the technical design and development of technology-based products
  • Experience working with technical architecture and owning technical writing
  • Experience in data analysis, data modelling, and data visualisation tools (e.g. SQL, Excel, Tableau, PowerBI etc.). Willing to learn database schemas, perform data manipulation tasks such as data cleaning, transformation and aggregation
  • Ability to facilitate end-to-end delivery, from research, solution options and design through to development, testing and release.
  • Ability of building, refining and prioritising an agile backlog for technical implementation of requirements
  • Proven experience working within technical functions, collaborating with Architects, Data Engineers/Analysts, Technical Designers, Software Developers etc.
  • Ability to learn different delivery methodologies such as Scrum, SAFE and Waterfall
  • Experience with presales & business development objectives, including RFP's, demos, presentations and bids


Ideally, you would also have:


  • Experience across different sectors, including in Public Sector, Utilities, Financial Services, Consumer Products and Retail
  • Experience supporting internal operations of the team through activities such as recruitment, client development, line management, delivering trainings and mentoring
  • Certifications across Business Analysis, Agile Delivery, User Centred Design etc.

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.