Business Analyst - W2

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