Business Analyst, Data, Insurance

City of London
1 year ago
Applications closed

Related Jobs

View all jobs

Business Data Analyst

Data Analyst - Power BI

Business Intelligence Analyst (Power BI / SQL)

Data Analyst

Senior Data Quality Analyst

Operations Data Analyst

Business Analyst is sought to join the growing data function of a buoyant insurance organisation based in the heart of the city. Within this role you will act as the key bridge between business stakeholders and technical teams - gathering and analysing business requirements, designing data-driven solutions, and supporting project delivery. You will contribute to improving data quality, aligning technical solutions with business objectives, and ensuring adherence to compliance and security standards.

This is a great opportunity to join a people-centric organisation with excellent opportunities for long term career progression.

Key Responsibilities:

  • Collaborate with business stakeholders to elicit, document and prioritise business requirements.

  • Work with technical teams to co-design scalable, efficient solutions leveraging cloud technologies (e.g., Azure, AWS) and data architectures such as data lakes and data mesh. Use data analysis to validate requirements and assist in creating visual representations such as wireframes and process models.

  • Participate in Agile delivery processes, including sprint planning and backlog refinement to ensure timely and impactful delivery.

  • Analyse data to uncover insights that inform strategies and drive operational efficiencies. Recommend process improvements based on data findings to enhance business value.

  • Facilitate workshops and meetings and translate technical concepts for non-technical stakeholders.

  • Support adherence to data governance, privacy, and security standards, collaborating with IT security teams to ensure data integrity and compliance.

    Key Skills & Experience:

  • Experience within a similar role working on data-driven projects.

  • Solid experience in business process mapping, data analysis and requirements gathering.

  • Proficiency in cloud platforms (Azure, AWS) and familiarity with data lakes and data mesh concepts.

  • Knowledge of Agile methodologies and tools like Jira or Confluence.

  • Strong documentation and communication skills for both technical and non-technical audiences.

  • Awareness of data governance, privacy, and regulatory standards (e.g., GDPR, Solvency II).

  • Experience in Agile environments with tools like Jira or Confluence.

    For a full consultation, send your CV to ARC IT Recruitment

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.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

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.