To improve visibility for certified products on a leading e-commerce platform, I led the development of a fully automated, serverless system that delivers product certification data for goods sold by brand partners. The system ensures eligible products are accurately labeled under the platform’s environmental recognition programs—such as Climate Pledge Friendly—boosting consumer trust and supporting ethical purchasing decisions.
Objectives
- Automate the daily submission of product certification data to an external retail API.
- Ensure partner-sold certified products are represented under sustainability-focused labeling.
- Improve efficiency, reliability, and scalability by removing manual upload processes.
- Enable tracking, monitoring, and recovery for failed submissions.
- Support high-volume, multi-marketplace operations with minimal maintenance overhead.
Business Value
- Certification Recognition: Products sold by partners receive proper labeling, reinforcing their certified status and appealing to environmentally conscious consumers.
- Operational Efficiency: Eliminates repetitive manual uploads, freeing up internal resources.
- Data Integrity: Reduces errors and maintains consistent representation of certified products across retail platforms.
Stakeholders
- Business and Commercial Teams managing partner certification recognition.
- Technical Engineering and Infrastructure Teams implementing and maintaining the solution.
- Initiative leads receiving sprint-based reporting and milestone updates.
Technical Architecture
Fully Serverless and Automated
The solution is 100% serverless, requiring no infrastructure provisioning. It is triggered, executed, monitored, and logged entirely via managed cloud services. Benefits include:
- Automatic scaling for large product batches (thousands of submissions per run).
- Cost-efficiency, paying only for actual compute and storage used.
- High availability and fault tolerance built into the platform.
- Rapid iteration and deployment using CI/CD workflows.
System Components
- Automated Daily Trigger
A scheduled task initiates the end-to-end product certification submission process each day. - Incremental Data Retrieval from Cloud Data Warehouse
Product data is retrieved from a cloud-hosted data warehouse using a dedicated query against a materialized view. Only new or updated products since the last successful submission are included, significantly optimizing API load and reducing unnecessary requests. - Data Preparation and Transformation
Product records are converted into the retail platform’s required format, ensuring each product is submitted individually per marketplace constraints. - Step-Based Parallel Orchestration
A state machine manages the flow, executing thousands of submission tasks in parallel using a map pattern. Each product submission is isolated, with dedicated error handling and logging. - Certification API Integration
Certification data is sent to the platform’s API, which enforces a strict rate limit and accepts only one product per marketplace per request. - Structured Logging and Monitoring
- Successful submissions are logged and archived for auditing.
- Failed requests are pushed to a Dead Letter Queue (DLQ) with detailed error metadata.
- Logs are also stored in a centralized database for tracking status codes, timestamps, and product identifiers.
- Environment Mocking for Testing
In non-production environments, a custom mock API simulates a mix of successful and failed responses for robust testing without live data exposure.
Authentication & Security
- Utilizes OAuth 2.0 tokens issued by the retail platform’s authorization system.
- All requests are signed using industry-standard AWS Signature Version 4.
- Secrets (e.g., client credentials, tokens) are managed securely in a cloud secrets manager.
Monitoring & Resilience
- Cloud-based Alarms. Triggers alerts when failure thresholds (e.g., >20%) are reached.
- Dead Letter Queue. Captures failed API interactions for reprocessing or analysis.
- Centralized Logs. All interactions are persisted with metadata for compliance and review.
Results
- End-to-End Automation. No human intervention required once set up.
- Reduced Load via Incremental Sync. Only delta changes are processed, improving speed and API efficiency.
- High Reliability. Automatic retries and logging provide full observability into the submission process.
- Scalable. Efficiently handles large catalogs with thousands of partner products.
Lessons Learned
- Mock testing environments proved essential due to lack of sandbox support from the external API.
- Filtering only updated records significantly reduced execution time and system costs.
- Clear logging and error messaging were vital for maintaining transparency and supporting partner needs.
Future Enhancements
- Build a visual dashboard for real-time certification submission monitoring.
- Automate reprocessing of failed jobs based on DLQ events.
- Extend the system to support multiple certification types or additional retailer APIs.
Tags: Automation, Serverless, AWS, Lambda, Step Functions, Sustainability, Redshift, API Integration, Retail Certification, Data Engineering, E-Commerce Enablement, Cloud Architecture, OAuth2, CI/CD, Monitoring, Partner Enablement, Incremental Sync, Product Certification, Dead Letter Queue, Fair Trade, Operational Efficiency