Lead QA Automation Engineer (Python)

Job summary

LTIMindtree is seeking a Lead QA Automation Engineer with Python expertise to drive quality assurance for a large-scale cloud-native media content platform. This role involves leading a QA team, defining strategy, and ensuring the reliability of distributed systems through deep technical expertise in API testing, data validation, and automation architecture. The successful candidate will implement shift-left strategies and collaborate with cross-functional leadership.

Benefits

Qualifications

• Proven track record as a QA Lead/Senior QA Engineer managing complex distributed systems and large-scale ETL/Data pipelines.
• Deep proficiency in JavaScript and/or Python for backend and API automation, including REST JSON validation.
• Expertise in the AWS ecosystem (S3, Lambda, API Gateway, ECS, ALB, SQS/SNS, Athena, OpenSearch/Elasticsearch).
• Hands-on experience with load testing tools (Locust) and modern CI/CD pipelines.
• Strong analytical thinking.
• Excellent cross-functional communication skills.
• Proven ability to mentor teams and debug complex production issues in dynamic environments.

Responsibilities

• Lead and mentor a team of QA engineers.
• Define the overall QA strategy.
• Champion shift-left practices in the SDLC.
• Align quality goals with Product, Engineering, and DevOps stakeholders.
• Own the testing strategy for the complete data lifecycle: Ingestion, Processing, Storage, API Exposure.
• Validate push/pull ingestion workflows, SNS/SQS scheduled jobs, and complex consumption endpoints, including keyword, category, and semantic search APIs.
• Validate millions of JSON records using AWS S3, Athena, SQL, and Glue Crawlers.
• Ensure the accuracy of metadata mappings, date logic, and licensable filtering across multiple publications.
• Design and maintain scalable automation frameworks using JavaScript (Jest) or Python (Pytest).
• Integrate API automation and JSON schema validation into CI/CD pipelines (AWS CodeBuild) with comprehensive reporting.
• Design and execute high-scale load tests using Locust.
• Analyze system bottlenecks (ALB, ECS, downstream services) to ensure optimal API latency (p95/p99) and semantic search throughput.
• Build and monitor dashboards (New Relic, OpenSearch) to track system health.
• Create and maintain high-quality Confluence documentation for test strategies, edge cases, and performance reports.
• Validate AI-powered features, including embedding generation (AWS Bedrock, Cohere) and vector-based retrieval in OpenSearch.
• Pioneer LLM-based automated test case generation and establish frameworks to validate semantic accuracy.
• Architect automated ephemeral test environments and implement contract testing or service virtualization.
• Test event-driven streaming systems and leverage knowledge of Docker and Kubernetes for test deployments.
• Work with and validate complex data models in Vector and Graph databases.

Skills

Languages

Relocation