About this role
At Zensar, we’re “experience-led everything”. We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures. Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus.
Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself.
We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status. QA/Test Engineer
Role Overview Responsible for ensuring the quality, accuracy, and reliability of all data pipelines, model outputs, and business deliverables. Works across the full delivery lifecycle from data ingestion through to final business consumable outputs.
Experience Required: 3–5 years
Key Responsibilities
• Design and execute comprehensive test plans covering data pipelines, model outputs, and business reports
• Perform data quality validation — completeness, accuracy, consistency, and referential integrity checks across all data sources
• Execute source-to-target reconciliation and backfill validation across all ingestion runs
• Validate ML model outputs against known historical data and SME-reviewed samples
• Build and maintain automated test suites for post-refresh pipeline health checks
• Execute end-to-end integration and performance testing across the full pipeline
• Manage UAT in collaboration with business stakeholders — track, manage, and retest all defects
• Validate security, access controls, and audit-trail requirements across all data outputs
• Validate AI-generated narrative outputs for factual accuracy and consistency with underlying model scores
Required Skills
• 3–5 years of experience in QA engineering, data testing, or software quality assurance
• Strong experience in data pipeline testing and data quality validation
• Proficiency in Python and SQL for test automation and reconciliation queries
• Experience with ML model output validation and testing approaches
• Familiarity with UAT management, defect tracking, and test case design
• Knowledge of cloud data platforms (Databricks, Snowflake) preferred
• Strong attention to detail with the ability to work across both technical and business workstreams
QA/Test Engineer
Role Overview Responsible for ensuring the quality, accuracy, and reliability of all data pipelines, model outputs, and business deliverables. Works across the full delivery lifecycle from data ingestion through to final business consumable outputs.
Experience Required: 3–5 years
Key Responsibilities
• Design and execute comprehensive test plans covering data pipelines, model outputs, and business reports
• Perform data quality validation — completeness, accuracy, consistency, and referential integrity checks across all data sources
• Execute source-to-target reconciliation and backfill validation across all ingestion runs
• Validate ML model outputs against known historical data and SME-reviewed samples
• Build and maintain automated test suites for post-refresh pipeline health checks
• Execute end-to-end integration and performance testing across the full pipeline
• Manage UAT in collaboration with business stakeholders — track, manage, and retest all defects
• Validate security, access controls, and audit-trail requirements across all data outputs
• Validate AI-generated narrative outputs for factual accuracy and consistency with underlying model scores
Required Skills
• 3–5 years of experience in QA engineering, data testing, or software quality assurance
• Strong experience in data pipeline testing and data quality validation
• Proficiency in Python and SQL for test automation and reconciliation queries
• Experience with ML model output validation and testing approaches
• Familiarity with UAT management, defect tracking, and test case design
• Knowledge of cloud data platforms (Databricks, Snowflake) preferred
• Strong attention to detail with the ability to work across both technical and business workstreams
QA/Test Engineer
Role Overview Responsible for ensuring the quality, accuracy, and reliability of all data pipelines, model outputs, and business deliverables. Works across the full delivery lifecycle from data ingestion through to final business consumable outputs.
Experience Required: 3–5 years
Key Responsibilities
• Design and execute comprehensive test plans covering data pipelines, model outputs, and business reports
• Perform data quality validation — completeness, accuracy, consistency, and referential integrity checks across all data sources
• Execute source-to-target reconciliation and backfill validation across all ingestion runs
• Validate ML model outputs against known historical data and SME-reviewed samples
• Build and maintain automated test suites for post-refresh pipeline health checks
• Execute end-to-end integration and performance testing across the full pipeline
• Manage UAT in collaboration with business stakeholders — track, manage, and retest all defects
• Validate security, access controls, and audit-trail requirements across all data outputs
• Validate AI-generated narrative outputs for factual accuracy and consistency with underlying model scores
Required Skills
• 3–5 years of experience in QA engineering, data testing, or software quality assurance
• Strong experience in data pipeline testing and data quality validation
• Proficiency in Python and SQL for test automation and reconciliation queries
• Experience with ML model output validation and testing approaches
• Familiarity with UAT management, defect tracking, and test case design
• Knowledge of cloud data platforms (Databricks, Snowflake) preferred
• Strong attention to detail with the ability to work across both technical and business workstreams