- Location
- India GCC-Puppalaguda Village
- Type
- Full-time
- Department
- Engineering
- Seniority
- Lead
- Closing date
- Today
- Source
- Workday
Description
We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.
Job Description: Data Quality & Test Automation Engineer
Technology Strategy & Enablement (TS&E) | Hyderabad, India
Location
Hyderabad, India
Employment Type
Full-Time
Experience Level
5-8 Years
Organization
Technology Strategy & Enablement (TS&E)
Position Overview
We are seeking a Data Quality & Test Automation Engineer to join TS&E. The role combines data-quality engineering, application and API automation, data-lake validation, and generative AI quality engineering to ensure enterprise data, analytics, and AI-generated insights are accurate, reliable, and trusted.
Key Responsibilities
Data Quality & Reconciliation
• Design and automate source-to-target reconciliation across source systems, ingestion, relational and non-relational databases, data lakes, AWS DocumentDB, APIs, and presentation layers.
• Validate data lake ingestion, partitioning, schema evolution, file completeness, and downstream consumption across approved AWS and GCP data services.
• Query JSON/document data in AWS DocumentDB or MongoDB-compatible platforms and use SQL to validate relational sources and analytical datasets.
• Validate completeness, accuracy, consistency, timeliness, uniqueness, organizational mappings, metrics, charts, filters, and drill-down results.
• Perform profiling, anomaly identification, defect triage, and root-cause analysis with Data Engineering and source-system owners.
Test Automation & Release Quality
• Design and maintain Playwright-based end-to-end automation for web and API experiences.
• Build API, integration, regression, smoke, negative, and cross-cloud test coverage.
• Create risk-based test plans, cases, traceability, defect documentation, and release-readiness summaries.
• Integrate automated tests and data checks into CI/CD pipelines and establish quality gates.
• Validate React, Node.js, and Python components using team-standard frameworks.
Generative AI Quality & Model Monitoring
• Validate AI-generated summaries, explanations, recommendations, and question-and-answer responses against approved source metrics.
• Define and track factual accuracy, groundedness, relevance, completeness, consistency, precision, recall, F1, and other applicable quality measures.
• Execute regression testing across approved model, prompt, retrieval, and configuration changes using benchmark and golden datasets.
• Monitor model and output drift, investigate quality degradation, and document release risk.
• Validate GCP- and AWS-hosted models, agent workflows, inference endpoints, and supporting services; GitHub Copilot testing may be included where relevant.
Required Qualifications & Skillset
• 5-8 years in quality engineering, test automation, data quality, or a related discipline.
• Hands-on Playwright expertise and Python and/or JavaScript/TypeScript automation skills.
• Experience with source-to-target reconciliation across relational, non-relational, and data-lake platforms.
• Experience querying AWS DocumentDB/MongoDB JSON models and aggregation pipelines; working SQL knowledge.
• Experience testing REST APIs, enterprise integrations, cloud data pipelines, and AWS/GCP services.
• Experience with generative AI evaluation, benchmark datasets, accuracy measures, regression testing, and model/output drift monitoring.
• Experience integrating quality controls into CI/CD pipelines.
Preferred Qualifications
• Experience with S3, Glue, Athena, Lake Formation, Redshift, or comparable GCP data services.
• Experience validating executive analytics and organizational drill-downs.
• Experience with Rally, Clarity, Workday, GitHub, GitHub Copilot, or similar platforms.
• Experience in regulated or audit-driven environments.
Success Profile
• Builds repeatable validation for data flows, applications, and AI-generated insights.
• Identifies defects and model-quality regressions before production.
• Provides clear release assessments and communicates risk in business terms.
• Works effectively across distributed US and Hyderabad teams.