Senior Business Analyst

Who You Will Be Working With

You will join the Data Trust Capability in Capgemini’s Insights and Data (I&D) business unit. Insights and Data is a global business unit covering Enterprise Data Management, Cloud Platforms, Enterprise Content Management and AI & Analytics.

Our team is one of the largest and most successful Data Management teams in the UK delivering innovative Data Management and Governance thought leadership to our clients.

The Enterprise Data Management provides services on Information Strategy, Data Governance, Master Data Management, Data Architecture, Data Migration and Lifecycle Management.

We help our clients build an enterprise-class data platform that allows them to move ahead in their journey of data and insights. Primarily working with leading software vendors like SAP, Informatica, IBM, Oracle et al, the team are first and foremost Consultants, putting client requirements and industry best practices at the heart of delivery.

The Focus Of Your Role

The Senior Business Analyst will lead the analysis and design of data and AI enabled change, working closely with business stakeholders, data teams and technology to translate business objectives into well defined requirements, processes, assisting designing, leading, and delivering enterprise scale data management, AI, compliance, risk, and security solutions. The role will ensure that the Data and AI solutions are grounded in high quality, well governed data, deliver measurable business outcomes, and comply with relevant data governance, regulatory and responsible AI standards.

Key Responsibilities

Key responsibilities include:

• •Lead cross-functional Business and Technology teams to define, analyse, and implement complex data-driven change.
• •Own and lead business analysis deliverables including requirement definition, gap analysis, process mapping, support test design and execution.
• •Understand, validate and Document functional and non-functional requirements.
• •Assist client leads in understanding the scope of their data initiatives.
• •Robust documentation on results and findings.
• •Raise key issues and risks in a timely manner to the Project Manager/Delivery Lead
• •Identify and refine use cases where data and AI can drive measurable business value
• •Challenge and clarify stakeholder requests (e.g. “we need AI”) into concrete business problems and decision points
• •Assess feasibility, risks, and data readiness for AI/ML/GenAI opportunities
• •Work with data owners, stewards and architects to identify required data sources, understand data lineage and constraints
• •Ensure requirements include data quality, metadata and governance needs (e.g. definitions, business rules, reference data, retention)
• •Contribute to and consume business glossaries, ontologies and data catalogues to ensure consistent terminology and understanding
• •Help ensure AI use cases comply with data privacy, security and regulatory requirements (e.g. GDPR, sector specific rules),
• •Translate business needs into requirements for AI/analytics solutions (e.g. decision support, predictions, recommendations, GenAI copilots),
• •Define functional and non functional requirements for AI capabilities:
• -performance, accuracy and reliability expectations
• -explainability / transparency needs
• -auditability, logging and traceability
• -Work with data scientists / engineers / AI teams to define test scenarios and acceptance criteria that reflect business reality (e.g. edge cases, thresholds for “good enough” accuracy).
• •Shape experiments / pilots (A/B tests, MVPs) to validate AI use cases quickly and safely.
• •Define and track KPIs / success metrics for AI enabled features (e.g. time saved, conversion rate uplift, error reduction, cost avoidance)
• •Support the business in interpreting AI performance results and making adoption / scaling decisions
• •Analyse process and role impacts from AI solutions (who does what differently, what should be automated vs supported).
• •Work with change, training and comms teams to support user adoption of AI tools and insights.
• •Champion responsible AI: help identify potential bias, fairness, transparency, and user trust issues, and ensure they are discussed and addressed.
• •Liaise with different levels of the hierarchy, especially with Development Leads, Quantitative Analytics and Risk Managers.
• •Follow good processes and practices, as defined by the team standards; ensuring that documentation is complete, comprehensive and signed off.
• •Assist with the prioritisation and planning of key deliverables.
• •Work in an agile manner whilst still understanding the needs of planning and forecasting the overall delivery.
• •Demonstrate strong leadership, senior stakeholder management, problem-solving, and decision-making skills.

You may also offer insights to the wider community through blogs, articles and social media.

At Capgemini, we believe in bringing your whole self to work. Equity, diversity, and inclusion are woven into our everyday culture, creating a welcoming and supportive environment for everyone.

What You Will Bring

As a Senior Business Analyst, you will have 8–12+ years in business analyst roles across data, AI, security, compliance, or cloud platforms, with proven experience delivering enterprise scale solutions. You’ll have a strong background in data management and data security/governance programmes.

Experience

• •Ability to delve deep into complex topics, analyse the impact and provide solutions
• •Ability to assimilate information quickly and make informed decisions
• •Strong initiative and accountability to investigate, resolve issues and own outcomes
• •Excellent verbal, written and presentation communication skills
• •Experience in working with cross-region teams and projects
• •Ability to lead medium to large sized teams.
• •Experience in delivering projects using an Agile methodology
• •Strong understanding of data concepts: data quality, master/reference data, metadata, lineage, governance.
• •Ability to interpret outputs from analytics, dashboards and models, and challenge them from a business perspective.
• •Experience working with data governance frameworks and roles (data owners, stewards etc.).

“Must-have” BA skills:

• •Requirements elicitation for functional and non-functional requirements
• •End-to-end business process analysis, modelling, and mapping.
• •Confident stakeholder management across business and technology.
• •Practical experience across Waterfall, Agile, and Scrum frameworks.
• •Strong consulting skills and client-facing engagement experience data-driven project experience
• •Cross-industry functional knowledge
• •Full delivery lifecycle experience: elicitation, design, build, testing, business change, operational support
• •Familiarity with AI and machine learning concepts (e.g. supervised vs unsupervised learning, prediction vs classification, model drift, basic evaluation metrics).
• •Comfortable working with requirements / modelling tools (BPMN, UML, user story tools, backlog management)
• •Experience eliciting and documenting data privacy, security and regulatory requirements
• •Ability to identify where AI use intersects with regulation, policy and data governance standards
• •Understanding of access controls, data minimisation, and appropriate use of data in AI systems
• •Ability to lead analysis across complex, cross functional initiatives, especially where AI affects multiple processes and teams
• •Strong stakeholder management – able to bridge business, data, architecture and engineering worlds
• •Comfortable challenging assumptions about AI (“shiny object syndrome”) and focusing on value, feasibility and risk

Further Info

“Good-to-have” skills:

• •BA/SCRUM/PM qualifications
• •MDM, data governance, data modelling exposure
• •Tools: JIRA, Confluence, etc.
• •Understanding of GenAI / LLM capabilities and limitations (hallucinations, prompt quality, data privacy, IP risks)