Senior Business Analyst
- J RAM IT Consulting Inc.
3 Hours Ago
NA
W2
Charlotte NC-NC
7-10 Years
Required Skills: User Stories, Starburst
Job Description
Senior Business Analyst – Data, GenAI & Agentic AI
We are seeking a Senior Business Analyst with strong business analysis, communication, facilitation, negotiation, data, and AI capabilities.
This role will bridge Business, Data, AI, and Technology to translate complex business problems into clear requirements, specifications, data needs, and AI capabilities. The successful candidate will combine traditional business analysis with data analysis, data virtualization, metadata and semantic analysis, context engineering, prompt engineering, GenAI/Agentic AI requirements, and specification-driven development.
The ideal candidate is comfortable moving from a business problem to the process, data, context, specifications, and AI behaviors required to deliver an effective solution.
Key Responsibilities
1. Business Analysis & Requirements
Lead requirements discovery, elicitation, analysis, validation, and documentation for complex business and technology initiatives.
Translate business objectives into capabilities, processes, requirements, Epics, Stories, specifications, acceptance criteria, and measurable outcomes.
Decompose complex problems into business processes, decisions, rules, data requirements, exceptions, dependencies, and system behaviors.
Define current-state and target-state business processes and identify opportunities for simplification, automation, and AI enablement.
Ensure requirements are clear, complete, testable, traceable, and ready for development.
Maintain traceability from business objective → capability → requirement → specification → implementation → validation.
2. Communication, Facilitation & Negotiation
Demonstrate exceptional written and verbal communication, translating complex business, data, and technology concepts for both senior stakeholders and delivery teams.
Lead discovery workshops, requirements sessions, story refinement, process reviews, data discussions, and solution-design conversations.
Facilitate cross-functional discussions to surface assumptions, resolve ambiguity, and drive decisions.
Navigate competing priorities and perspectives and negotiate outcomes that balance business value, delivery feasibility, risk, and technology constraints.
Constructively challenge requirements and assumptions while building alignment across Business and Technology stakeholders.
Partner effectively with Product Owners, SMEs, Data Owners, Data Stewards, Architects, Developers, Data Engineers, AI Engineers, Risk, and Control partners.
Demonstrate exceptional written and verbal communication, translating complex business, data, and technology concepts for both senior stakeholders and delivery teams.
Lead discovery workshops, requirements sessions, story refinement, process reviews, data discussions, and solution-design conversations.
Facilitate cross-functional discussions to surface assumptions, resolve ambiguity, and drive decisions.
Navigate competing priorities and perspectives and negotiate outcomes that balance business value, delivery feasibility, risk, and technology constraints.
Constructively challenge requirements and assumptions while building alignment across Business and Technology stakeholders.
Partner effectively with Product Owners, SMEs, Data Owners, Data Stewards, Architects, Developers, Data Engineers, AI Engineers, Risk, and Control partners.
3. Data Analysis & Data Requirements
Identify and analyze the business data required to support AI agents, analytics, continuous monitoring, and other technology capabilities.
Translate business requirements into detailed data requirements, including:
Source systems
Critical data elements
Business definitions
Relationships and mappings
Business and transformation rules
Refresh/timeliness requirements
Data ownership
Data quality expectations
Analyze structured and unstructured data and work with SMEs to determine its business meaning and fitness for use.
Partner with Data Engineering and Architecture teams to connect and expose relevant data sources through data virtualization platforms such as Starburst.
Maintain traceability from business requirement → business concept → data source → data element → transformation/business rule → solution/AI consumption.
Identify and analyze the business data required to support AI agents, analytics, continuous monitoring, and other technology capabilities.
Translate business requirements into detailed data requirements, including:
Source systems
Critical data elements
Business definitions
Relationships and mappings
Business and transformation rules
Refresh/timeliness requirements
Data ownership
Data quality expectations
Analyze structured and unstructured data and work with SMEs to determine its business meaning and fitness for use.
Partner with Data Engineering and Architecture teams to connect and expose relevant data sources through data virtualization platforms such as Starburst.
Maintain traceability from business requirement → business concept → data source → data element → transformation/business rule → solution/AI consumption.
4. Data Quality, Metadata & Governance
Assess data for accuracy, completeness, consistency, timeliness, validity, and fitness for purpose.
Identify data-quality gaps that could affect business outcomes, analytics, or AI responses.
Partner with Data Owners, Data Stewards, and governance teams to define and maintain business metadata, definitions, classifications, ownership, lineage, and data-quality requirements.
Support data governance and quality management using platforms such as Collibra.
Ensure critical data is discoverable, understandable, governed, traceable, and appropriately contextualized before it is consumed by AI and downstream solutions.
5. Metadata, Semantic Data & AI-Ready Data
Analyze business terminology, metadata, data relationships, and definitions to establish the semantic meaning and context of enterprise data.
Work with business and technology partners to establish common business definitions, relationships, classifications, and contextual attributes.
Understand concepts such as business glossaries, metadata, lineage, taxonomies, ontologies, semantic models, and semantic relationships and their relevance to AI solutions.
Help transform enterprise information into AI-ready data and knowledge that is understandable and consumable by GenAI and Agentic AI solutions.
Define the relationship between business concepts, business processes, policies, data, metadata, and AI context.
Analyze business terminology, metadata, data relationships, and definitions to establish the semantic meaning and context of enterprise data.
Work with business and technology partners to establish common business definitions, relationships, classifications, and contextual attributes.
Understand concepts such as business glossaries, metadata, lineage, taxonomies, ontologies, semantic models, and semantic relationships and their relevance to AI solutions.
Help transform enterprise information into AI-ready data and knowledge that is understandable and consumable by GenAI and Agentic AI solutions.
Define the relationship between business concepts, business processes, policies, data, metadata, and AI context.
6. GenAI & Agentic AI Requirements
Translate business use cases into requirements for Generative AI and Agentic AI solutions.
Define agent:
Objectives and expected outcomes
Roles and responsibilities
Inputs and outputs
Tasks and workflows
Decision boundaries
Data and knowledge requirements
Human-in-the-loop interactions
Business rules and constraints
Exceptions and escalation paths
Guardrails
Success and evaluation criteria
Work with business and engineering teams to define where AI should inform, recommend, augment, or automate business activities.
Convert business expectations into testable AI acceptance criteria, including expectations for grounding, relevance, completeness, consistency, and appropriate behavior.
Translate business use cases into requirements for Generative AI and Agentic AI solutions.
Define agent:
Objectives and expected outcomes
Roles and responsibilities
Inputs and outputs
Tasks and workflows
Decision boundaries
Data and knowledge requirements
Human-in-the-loop interactions
Business rules and constraints
Exceptions and escalation paths
Guardrails
Success and evaluation criteria
Work with business and engineering teams to define where AI should inform, recommend, augment, or automate business activities.
Convert business expectations into testable AI acceptance criteria, including expectations for grounding, relevance, completeness, consistency, and appropriate behavior.
7. Context Engineering & Prompt Engineering
Define the business context AI agents require to accurately understand and perform their tasks.
Identify relevant processes, policies, terminology, business rules, metadata, data relationships, historical context, and knowledge sources.
Develop and refine prompts/instructions that clearly establish intent, context, constraints, expected outputs, and examples.
Partner with AI Engineering teams to improve how enterprise context is assembled and provided to AI solutions.
Help determine what information should be provided directly as instructions versus retrieved dynamically from enterprise knowledge and data sources.
8. Specification-Driven Development
Develop precise, structured, implementation-ready specifications that minimize ambiguity for engineering teams and AI-assisted development tools.
Go beyond high-level user stories to explicitly document:
Expected behavior
Business rules
Decision logic
Data requirements
Inputs/outputs
Interfaces
Exceptions
Dependencies
Acceptance criteria
Non-functional requirements
Maintain alignment between business requirements, data specifications, AI behavior, technical implementation, and validation.
Partner closely with Engineering throughout development to clarify specifications and reduce requirements-related rework.
Develop precise, structured, implementation-ready specifications that minimize ambiguity for engineering teams and AI-assisted development tools.
Go beyond high-level user stories to explicitly document:
Expected behavior
Business rules
Decision logic
Data requirements
Inputs/outputs
Interfaces
Exceptions
Dependencies
Acceptance criteria
Non-functional requirements
Maintain alignment between business requirements, data specifications, AI behavior, technical implementation, and validation.
Partner closely with Engineering throughout development to clarify specifications and reduce requirements-related rework.
Required Qualifications
- Significant experience as a Senior Business Analyst, Business Systems Analyst, Product Analyst, or equivalent supporting complex technology initiatives.
- Strong expertise in business analysis, requirements engineering, process analysis, and requirements decomposition.
- Exceptional communication, facilitation, negotiation, stakeholder management, and influencing skills.
- Strong analytical skills with the ability to work across business processes, data, and technology.
- Experience defining data requirements and analyzing structured and unstructured data.
- Understanding of data quality, metadata, lineage, business definitions, semantic relationships, and data governance.
- Experience with Agile delivery, including Epics, Stories, acceptance criteria, refinement, and iterative development.
- Working knowledge of GenAI, LLMs, Agentic AI, context engineering, and prompt engineering.
- Ability to translate AI use cases into clear business requirements, agent behaviors, guardrails, data/context needs, and acceptance criteria.
- Understanding of specification-driven development and the importance of precise, structured requirements for both traditional and AI-assisted software development.
Preferred / Differentiating Skills
- Experience with Starburst or similar data virtualization/query platforms.
- Experience with Collibra or similar data governance, catalog, metadata, and data-quality platforms.
- Experience with enterprise GenAI platforms such as Microsoft Copilot, Copilot Studio, Azure AI, or comparable technologies.
- Familiarity with concepts such as RAG, semantic search, vector search, enterprise knowledge retrieval, AI agents, and orchestration.
- Experience with Jira or similar Agile delivery tools.
- Experience working in a highly regulated environment where data governance, traceability, controls, explainability, and human oversight are important.
- Audit, Risk, Compliance, or continuous risk monitoring experience is a strong plus.
The Senior BA will help ensure that the organization has not only well-defined requirements, but also the trusted data, business context, semantic meaning, specifications, and guardrails required for GenAI and Agentic AI solutions to deliver meaningful business outcomes.
Main change: Integrated the data responsibilities throughout the role rather than creating a standalone “data BA” requirement. That makes Starburst, Collibra, data quality/governance, metadata/semantics, context engineering, and AI-ready data part of the end-to-end BA lifecycle, while keeping data engineering implementation with the appropriate technical teams.Senior Business Analyst – Data, GenAI & Agentic AI
Main change: Integrated the data responsibilities throughout the role rather than creating a standalone “data BA” requirement. That makes Starburst, Collibra, data quality/governance, metadata/semantics, context engineering, and AI-ready data part of the end-to-end BA lifecycle, while keeping data engineering implementation with the appropriate technical teams.Senior Business Analyst – Data, GenAI & Agentic AI
