Business Analyst - Gen AI

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.

Requirements:

· 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.

Responsibilities:

· 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:

o source systems and critical data elements (CDEs)

o business definitions and mappings

o transformation rules and refresh needs

o 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:

o objectives, roles, and inputs/outputs

o workflows and decision boundaries

o data and knowledge needs

o human-in-the-loop interactions

o business rules, exceptions and escalation paths, and guardrails

o 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:

o behavior, business rules, and decision logic

o data, inputs/outputs, and interfaces

o exceptions and dependencies

o acceptance criteria and non-functional requirements

· Partnering with Engineering throughout development to clarify specifications and reduce rework.

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