Senior Business Analyst – Data, GenAI & Agentic AI

We’re looking for a hands-on Senior Business Analyst for a high-visibility Data and GenAI/Agentic AI program. The role connects Business, Data, AI, and Technology. You’ll take a complex business problem and turn it into processes, requirements, data needs, AI context, and implementation-ready specifications.

This position suits a self-starter with strong analytical, communication, facilitation, and negotiation skills who can work effectively with senior business stakeholders and delivery teams.

The role requires:

• Proficiency translating business problems into processes, requirements, data, AI context, and specifications
• Strong data skills to define and validate the data that AI agents and analytics depend on
• Working knowledge of GenAI, LLMs, Agentic AI, context engineering, and prompt engineering
• A specification-driven mindset: precise, structured, testable requirements for traditional and AI-assisted development.

Responsibilities include:

• Leading requirements discovery, elicitation, and documentation, translating business objectives into Epics, Stories, specifications, and acceptance criteria
• Defining current-state and target-state processes and identifying opportunities for simplification, automation, and AI enablement
• Maintaining end-to-end traceability, from business objective through requirement, specification, and implementation to validation
• Leading discovery workshops, refinement sessions, and solution-design discussions; resolving ambiguity and negotiating priorities across Business and Technology
• Defining data requirements, including:
• source systems and critical data elements (CDEs)
• business definitions and mappings
• transformation rules and refresh needs
• ownership and data quality expectations
• Analyzing structured and unstructured data with SMEs
• Partnering with Data Engineering to expose data sources through Starburst (or similar data virtualization tools)
• Assessing data quality and working with Data Owners and Stewards to maintain metadata, lineage, classifications, and governance in Collibra (or similar)
• Establishing business glossaries, taxonomies, ontologies, and semantic relationships to make enterprise data AI-ready
• Defining AI agent requirements:
• objectives, roles, and inputs/outputs
• workflows and decision boundaries
• data and knowledge needs
• human-in-the-loop interactions
• business rules, exceptions and escalation paths, and guardrails
• evaluation criteria
• Defining where AI should inform, recommend, augment, or automate business activities
• Writing testable AI acceptance criteria covering grounding, relevance, completeness, consistency, and appropriate behavior
• Designing and refining prompts, and defining the business context agents need: policies, terminology, rules, metadata, and knowledge sources
• Deciding with AI Engineering what is provided as instructions versus retrieved dynamically from enterprise knowledge and data
• Writing implementation-ready specifications that cover:
• behavior, business rules, and decision logic
• data, inputs/outputs, and interfaces
• exceptions and dependencies
• acceptance criteria and non-functional requirements
• Partnering with Engineering throughout development to clarify specifications and reduce rework.

Required:

• Significant experience as a Senior BA, BSA, Product Analyst, or equivalent on complex technology initiatives
• Strong expertise in requirements engineering, process analysis, and requirements decomposition
• Exceptional communication, facilitation, negotiation, stakeholder management, and influencing skills
• Experience defining data requirements and analyzing structured and unstructured data
• Understanding of data quality, metadata, lineage, business definitions, semantic relationships, and data governance
• Agile delivery experience: Epics, Stories, acceptance criteria, refinement, Jira
• Working knowledge of GenAI, LLMs, Agentic AI, context engineering, and prompt engineering
• Ability to translate AI use cases into requirements, agent behaviors, guardrails, data and context needs, and acceptance criteria
• Understanding of specification-driven development.

Preferred / huge plus:

• Starburst or similar data virtualization or query platforms
• Collibra or similar governance, catalog, metadata, or data quality platforms
• Microsoft Copilot, Copilot Studio, Azure AI, or comparable enterprise GenAI platforms
• RAG, semantic and vector search, enterprise knowledge retrieval, AI agents, and orchestration
• Experience in a highly regulated environment (traceability, controls, explainability, human oversight)
• Audit, Risk, Compliance, or continuous risk monitoring experience