IND Lead Software Engineer

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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.