Senior Business Data Analyst
Austin, TX – LOCAL CANDIDATES ONLY
Hybrid Schedule
Onsite: Mon, Tue, Thu
Remote: Wed, Fri
Interview Mode: In-Person Only
Location: Austin, TX (Local candidates only)
Role Overview
experienced Business Data Analyst to analyze business requirements, evaluate current processes, and translate complex data-driven needs into clear, structured requirements for system enhancements and new solutions.
This role requires strong analytical thinking, excellent documentation abilities, and the capability to collaborate with business stakeholders, product owners, and technical teams. You will play a key role in writing Epics, User Stories, acceptance criteria, and data specifications to support modernization efforts.
Key Responsibilities
Analyze business objectives, current system workflows, and operational processes
Document user needs, functional requirements, and detailed system specifications
Perform data analysis, process mapping, and identify areas for system improvement
Develop Epics, User Stories, and acceptance criteria for Agile teams
Create and maintain data dictionaries, BRDs, data mappings, and documentation
Lead data-focused meetings, workshops, and JAD sessions
Present visual dashboards, data models, and findings to stakeholders
Collaborate closely with cross-functional teams (business, PO, QA, development)
Minimum Qualifications
✔ 8+ years as an IT Business Data Analyst
✔ Strong analytical, documentation, and data workflow design skills
✔ Experience writing Epics, User Stories, and acceptance criteria
✔ Ability to design dashboards & visual data models
✔ Experience creating data dictionaries, BRDs, data mapping specs
✔ Proven ability to lead data meetings & resolve data-related issues
✔ Experience working with technical & non-technical teams
Preferred Qualifications
Bachelor's degree in Data Science, CS, IS, or related fields
Experience with SNAP, Medicaid, TANF eligibility rules
Experience with integrated eligibility case management systems
Prior experience leading JAD sessions
Ability to manage multiple data projects simultaneously
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