Role: Senior Business Analyst – Data, GenAI & Agentic AI (Onshore)
Location: Pennington, NJ (Min 3 days work from office every week) - (preferred is Pennington but also open for Charlotte, NC)
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
