{"id":26684562,"url":"https://github.com/nanlabs/cloud-data-engineer-challenge","last_synced_at":"2026-04-30T20:32:54.639Z","repository":{"id":283003433,"uuid":"950368280","full_name":"nanlabs/cloud-data-engineer-challenge","owner":"nanlabs","description":"🚀 Cloud Data Engineer Challenge – Build an event-driven pipeline using AWS S3, Lambda, PostgreSQL (PostGIS) and API Gateway. Use IaC to deploy your solution. Bonus points for CI/CD, monitoring, and Docker support. 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If you choose a different tool or a combination of tools, **justify your decision!**\n\n## ⚡ Challenge Overview\n\nYour task is to deploy the following infrastructure on AWS:\n\n\u003e 🎯 **Key Objectives:**\n\n- **An S3 bucket** that will receive data files as new objects.\n- **A Lambda function** that is triggered by a `PUT` event in the S3 bucket.\n- **The Lambda function must:**\n  - Process the ingested data and perform a minimal aggregation.\n  - Store the processed data in a **PostgreSQL database with PostGIS enabled**.\n  - Expose an API Gateway endpoint (`GET /aggregated-data`) to query and retrieve the aggregated data.\n- **A PostgreSQL database** running in a private subnet with PostGIS enabled.\n- **Networking must include:** VPC, public/private subnets, and security groups.\n- **The Lambda must be in a private subnet** and use a NAT Gateway in a public subnet for internet access 🌍\n- **CloudWatch logs** should capture Lambda execution details and possible errors.\n\n\u003e [!IMPORTANT]\n\u003e Ensure that your solution is modular, well-documented, and follows best practices for security and maintainability.\n\n## 📌 Requirements\n\n### 🛠 Tech Stack\n\n\u003e ⚡ **Must Include:**\n\n- **IaC:** Any tool of your choice (**Terraform preferred**, but others are allowed if justified).\n- **AWS Services:** S3, Lambda, API Gateway, CloudWatch, PostgreSQL with PostGIS (RDS or self-hosted on EC2).\n\n### 📄 Expected Deliverables\n\n\u003e 📥 **Your submission must be a Pull Request that includes:**\n\n- **An IaC module** that deploys the entire architecture.\n- **A `README.md`** with deployment instructions and tool selection justification.\n- **A working API Gateway endpoint** that returns the aggregated data stored in PostgreSQL.\n- **CloudWatch logs** capturing Lambda execution details.\n- **Example input files** to trigger the data pipeline (placed in an `examples/` directory).\n- **A sample event payload** (JSON format) to simulate the S3 `PUT` event.\n\n\u003e [!TIP]\n\u003e Use the `docs` folder to store any additional documentation or diagrams that help explain your solution.\n\u003e Mention any assumptions or constraints in your `README.md`.\n\n## 🌟 Nice to Have\n\n\u003e 💡 **Bonus Points For:**\n\n- **Data Quality \u0026 Validation**: Implementing **schema validation before storing data in PostgreSQL**.\n- **Indexing \u0026 Query Optimization**: Using **PostGIS spatial indexing** for efficient geospatial queries.\n- **Monitoring \u0026 Alerts**: Setting up **AWS CloudWatch Alarms** for S3 event failures or Lambda errors.\n- **Automated Data Backups**: Creating periodic **database backups to S3** using AWS Lambda or AWS Backup.\n- **GitHub Actions for validation**: Running **`terraform fmt`, `terraform validate`**, or equivalent for the chosen IaC tool.\n- **Pre-commit hooks**: Ensuring linting and security checks before committing.\n- **Docker for local testing**: Using **Docker Compose to spin up**:\n  - Running a local PostgreSQL database with PostGIS to simulate the cloud environment 🛠\n  - Providing a local S3-compatible service (e.g., MinIO) to test file ingestion before deployment 🖥\n\n\u003e [!TIP]\n\u003e Looking for inspiration or additional ideas to earn extra points? Check out our **[Awesome NaNLABS repository](https://github.com/nanlabs/awesome-nan)** for reference projects and best practices! 🚀\n\n## 📥 Submission Guidelines\n\n\u003e 📌 **Follow these steps to submit your solution:**\n\n1. **Fork this repository.**\n2. **Create a feature branch** for your implementation.\n3. **Commit your changes** with meaningful commit messages.\n4. **Open a Pull Request** following the provided template.\n5. **Our team will review** and provide feedback.\n\n## ✅ Evaluation Criteria\n\n\u003e 🔍 **What we'll be looking at:**\n\n- **Correctness and completeness** of the **data pipeline**.\n- **Use of best practices for event-driven processing** (S3 triggers, Lambda execution).\n- **Data transformation \u0026 aggregation logic** implemented in Lambda.\n- **Optimization for geospatial queries** using PostGIS.\n- **Data backup \u0026 integrity strategies** (optional, e.g., automated S3 backups).\n- **CI/CD automation using GitHub Actions and pre-commit hooks** (optional).\n- **Documentation clarity**: Clear explanation of data flow, transformation logic, and infrastructure choices.\n\n## 🎯 **Good luck and happy coding!** 🚀\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnanlabs%2Fcloud-data-engineer-challenge","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnanlabs%2Fcloud-data-engineer-challenge","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnanlabs%2Fcloud-data-engineer-challenge/lists"}