Senior Business Data Quality Analyst (Business Analyst III) - 26-06919

NavitasPartners•Austin, TX

2d•Onsite

About The Position

We are seeking an experienced Senior Business Data Quality Analyst to serve as a key liaison between technical teams and business stakeholders within a large healthcare and analytics environment. This role focuses on data profiling, data quality analysis, business requirements gathering, analytics support, AI-assisted data exploration, and promoting data literacy across the organization. The ideal candidate will possess strong SQL expertise, extensive experience working with complex datasets, exceptional communication skills, and the ability to translate technical findings into actionable business insights for non-technical audiences.

Requirements

8+ years gathering business requirements and translating complex data acquisition and operational needs into actionable analytics solutions.
8+ years explaining technical findings and data limitations to business users and leadership.
8+ years working as: Senior Business Analyst, Systems Analyst, Data Analyst, Data Liaison in complex data environments.
8+ years of advanced SQL experience including: Data extraction, Data profiling, Data manipulation, Data enrichment
8+ years collaborating with business users, analytics teams, and IT leadership to develop and validate data profiles and data lineage.
Excellent communication, presentation, facilitation, and stakeholder management skills.
Must currently reside in Texas
In-person interview required

Nice To Haves

5+ years working with: Business Intelligence solutions, Data Warehouses, Enterprise Analytics platforms
5+ years serving as the primary point of contact for business data profiling initiatives.
5+ years working in: Healthcare, Health and Human Services, Government, Highly regulated environments
5+ years of experience with: Data Governance, Data Quality Management, Data Stewardship Practices
Experience with data profiling tools such as: Snowflake Cortex, Toad, Similar data analysis platforms
Experience promoting data literacy initiatives.
Experience training and mentoring staff with varying levels of technical expertise.
Experience creating educational content and documentation.
Experience developing AI prompts and query strategies.
Experience validating AI-generated outputs and ensuring data accuracy.
Familiarity with AI-assisted analytics and reporting workflows.

Responsibilities

Gather and document business requirements related to data quality and analytics initiatives.
Analyze complex data domains and identify data quality issues, anomalies, and improvement opportunities.
Translate operational and business requirements into actionable data profiling and reporting solutions.
Support business users in understanding data sources, data lineage, and reporting requirements.
Extract, profile, analyze, and validate data from multiple complex internal and external data sources.
Perform advanced SQL queries for data extraction, enrichment, validation, and analysis.
Collaborate with technical and business teams to develop data quality metrics and reporting solutions.
Validate data profiles and support analytics development initiatives.
Work with business stakeholders and analytics teams to create data quality visualizations and reporting outputs.
Ensure compliance with data governance, reporting standards, and regulatory requirements.
Support business intelligence and data warehouse initiatives.
Develop and refine AI prompts and query strategies for data retrieval and analysis.
Create reusable prompt libraries and reporting templates.
Guide non-technical users in effectively leveraging AI tools for data exploration.
Validate AI-generated outputs and identify inaccuracies or hallucinations.
Promote responsible AI usage within analytics workflows.
Develop training materials, user guides, and documentation.
Conduct workshops and training sessions for staff with varying levels of technical expertise.
Promote data literacy and self-service analytics capabilities across the organization.
Help establish a data-driven culture through education and enablement.
Serve as the primary liaison between business units, program areas, and technical teams.
Explain technical findings and data limitations in clear, business-friendly language.
Provide guidance on data interpretation, quality concerns, and reporting outcomes.
Support multiple analytics initiatives simultaneously in an Agile environment.