Senior Business Analyst -Technical

Wolters Kluwer•Coppell, TX

10h

About The Position

The Senior Technical Business Analyst – Product Engineering translates business objectives into detailed, development-ready technical requirements and supports Spec-Driven Development, AI-assisted software engineering, and AI-powered product innovation. The role bridges Product Management, business stakeholders, architecture, and engineering teams, helping modernize how requirements are gathered, documented, validated, and maintained throughout the SDLC. It creates structured specifications that reduce ambiguity and rework, improve developer productivity, and support AI-assisted development workflows. The analyst also uses AI-assisted tools to reverse engineer existing applications and discover undocumented business rules, workflows, APIs, databases, dependencies, and integration patterns. The role supports application modernization by documenting current and target states and helps define and validate AI-powered product capabilities, including generative AI, conversational AI, intelligent search, recommendation systems, and workflow automation. It develops evaluation criteria and validation scenarios for AI solutions and communicates requirements and findings to both technical and business audiences. The role requires a bachelor's or master's degree in Business, Computer Science, Software Engineering, or a related field; at least 8 years of business or systems analysis experience; and at least 3 years working directly with software engineering teams. Compensation is $71,300–$124,500 USD, with bonus eligibility. The listed range is based on the position's primary location, and the offer may vary based on factors including skills, experience, and hiring location.

Requirements

Bachelor's or master's degree in Business, Computer Science, Software Engineering, or a related field.
8+ years of experience as a business analyst or systems analyst.
3+ years of experience working directly with software engineering teams.
Advanced requirements elicitation and analysis skills.
Experience creating user stories and managing backlogs.
Business process modeling and workflow analysis skills.
Experience with requirements traceability and documentation management.
Ability to define functional and non-functional requirements.
Business case development and stakeholder management skills.
Strong understanding of the software development lifecycle (SDLC).
Experience working closely with software engineering and architecture teams.
Knowledge of APIs, microservices, cloud platforms, system integrations, and modern application architectures.
Understanding of database concepts, data flows, and system interactions.
Familiarity with Agile development methodologies and product delivery frameworks.
Knowledge or experience in AI-assisted requirements engineering, Spec-Driven Development, prompt engineering, AI-oriented SDLC, AI testing and evaluation frameworks, generative AI product development, reverse engineering and documentation automation, developer productivity platforms, and AI coding assistants.
Experience with GitHub Copilot, OpenAI ChatGPT, Claude, or similar AI-enabled engineering tools.
Strong analytical and critical-thinking capabilities and ability to turn complex business problems into structured requirements.
Excellent written and verbal communication skills.
Strong facilitation and stakeholder management abilities.
Experience leading workshops and requirements discovery sessions.

Nice To Haves

Experience supporting enterprise SaaS products and digital platforms.
Exposure to AI-powered product development and evaluation processes.
Experience with tools such as Azure DevOps, Jira, Confluence, Miro, and GitHub.
Experience with process mining, application modernization, or legacy platform transformation initiatives.
Experience creating specifications that directly support AI-assisted software development.

Responsibilities

Partner with Product Management, Engineering, Architects, UX, and business stakeholders to understand objectives and translate them into actionable technical requirements.
Elicit, analyze, document, and validate functional and non-functional requirements.
Create user stories, use cases, business process flows, acceptance criteria, and technical specifications.
Ensure requirements are complete, traceable, testable, and aligned with business outcomes.
Facilitate requirements workshops, stakeholder reviews, and backlog refinement sessions.
Author and maintain engineering-ready specifications for development teams, architects, QA teams, and AI-assisted development tools.
Define functional behaviors, workflows, business rules, data models, integration requirements, and API contracts.
Create structured specifications that support automated generation of engineering artifacts and development tasks.
Establish requirement templates, standards, and governance practices for Spec-Driven Development.
Collaborate with engineering teams to convert requirements into implementation-ready work items and improve requirement quality to reduce delivery risk and rework.
Use AI-assisted tools to analyze and reverse engineer applications, workflows, APIs, databases, and business processes; identify undocumented rules, dependencies, variations, and integration patterns.
Convert legacy functionality into modern technical specifications and future-state requirements; document current-state and target-state processes, systems, and architectures.
Collaborate with engineering and architecture teams to build reusable knowledge repositories and functional documentation.
Use AI tools to accelerate requirement discovery, documentation, analysis, traceability, and validation; generate and evaluate user stories, acceptance criteria, process flows, test scenarios, and specifications.
Develop prompts and establish responsible-use practices for AI throughout the requirements lifecycle.
Collaborate with Product and AI teams to define requirements, prompts, evaluation criteria, guardrails, and business validation rules for AI-powered product capabilities.
Support the design and validation of generative AI, conversational AI, intelligent search, recommendation, and workflow automation features, defining expected behaviors, interactions, grounding requirements, and success metrics.
Create business-oriented evaluation criteria, validation scenarios, test cases, and benchmark datasets for AI applications; assess quality, accuracy, relevance, safety, and business effectiveness.
Analyze AI evaluation results and recommend improvements to prompts, retrieval strategies, workflows, and user experiences; establish quality metrics and acceptance criteria for AI-generated outcomes.
Act as liaison between technical and business teams, communicate complex concepts clearly, and support leadership with business cases, impact assessments, process improvement opportunities, and product strategy initiatives.