QA Analyst

Bounteous is a global AI Services firm where agentic engineering and human experience converge to deliver transformative business outcomes for the enterprise. We help organizations design, build, and scale AI-driven products, platforms, and processes. With more than 5,000 team members worldwide, Bounteous delivers AI that sticks, powering adoption and outcomes that move organizations from experimentation to true transformation. Bounteous is backed by New Mountain Capital, a New York-based growth-oriented investment firm that emphasizes business building. QA Analyst Contract LATAM - Mexico Key Responsibilities: • Quality Assurance Execution: Execute comprehensive test cases, identify data discrepancies, and document defects to ensure the integrity and accuracy of our data pipelines and reporting structures. • Data Validation & Verification: Query and analyze complex data sets to verify that information processing through our systems aligns perfectly with established business logic and specifications. A strength here is a must. • Process Optimization: Contribute to maintaining and continuously improving QA documentation, standard operating procedures, and testing frameworks to ensure repeatable, scalable processes. Qualifications & Requirements: • Professional QA Experience: 1–3 years of experience operating within a dedicated software or data quality assurance environment, with a proven track record of accurate defect tracking and reporting. • Data Literacy: Demonstrated experience working directly with data structures, including a foundational understanding of relational databases and data manipulation. • Analytical & Critical Thinking: Strong problem-solving skills with a keen eye for detail and the ability to conduct root-cause analysis on data anomalies. • Communication Skills: Excellent verbal and written communication skills, with the ability to articulate technical issues and defects clearly to cross-functional stakeholders. Preferred Skills: Experience with the following tools is considered an asset, but is not required: • Foundational knowledge of SQL or exposure to cloud-based data warehouses such as Google BigQuery. • Familiarity with enterprise data visualization and business intelligence tools (e.g., Looker, Tableau). • Basic exposure to Python scripting or the ability to read and interpret code.