Overview
Automation and validation of an enterprise retail data and search ecosystem, focused on ensuring correctness, consistency, and reliability across large-scale data ingestion and search platforms.
This project addressed high-risk data flows where incorrect transformations or indexing could directly impact product discovery, SEO, and customer experience.
What I worked on
- ETL validation for large retail datasets flowing from upstream commerce systems into a search platform
- Verified data extraction, transformation, and loading logic to ensure accuracy across ingestion stages
- Validated field mapping and data integrity between source systems and the search index
- Ensured data consistency in DynamoDB after ingestion and transformation
- Automated validation of search metadata, including:
- Metadata attributes
- URL slugs
- SEO-related data (SEODS)
Search & sitemap automation
- Designed and implemented a scalable automation framework for:
- Sitemap validation
- Search metadata correctness
- SEO-critical attributes
- Built the framework from scratch using Playwright (TypeScript) for reliability and extensibility
- Ensured automated coverage for scenarios impacting search discoverability and indexing
Engineering approach
- Developed Cypress-based automation frameworks for API and data validation
- Automated backend REST API testing using Cypress and the AWS SDK
- Validated DynamoDB tables after ingestion and scan operations
- Tested AWS Lambda and Step Functions responsible for data processing workflows
- Integrated automation into GitLab CI/CD pipelines with:
- Allure reporting
- Clear execution signals
- CI-friendly test orchestration
Quality & reliability focus
- Emphasized data correctness over UI-only validation
- Built tests to detect:
- Broken transformations
- Incomplete ingestion
- Incorrect field mappings
- Search metadata regressions
- Ensured pipelines provided fast, deterministic feedback for data and search-related changes
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Outcome
- Improved confidence in enterprise data ingestion and search reliability
- Reduced production issues related to incorrect indexing and metadata
- Established a scalable automation foundation for validating future data pipelines and search enhancements