Enhancing Data Science & Engineering Backend for Sustainability Analytics Platform

The client faces challenges with maintaining and improving the quality of their existing backend codebase, which was initially developed by a data scientist and has deteriorated in quality over time. They struggle with efficient data analysis, calculation of environmental metrics, and scaling their solutions. Their current infrastructure lacks the robustness needed to support accurate and reliable sustainability data processing, impeding their ability to serve large corporate clients effectively and scale their operations.

A mid-sized startup specializing in analyzing and calculating environmental sustainability metrics such as carbon footprint, water, and chemical usage for large enterprises. The company lacks in-house technical capacity and requires backend development support to refine and scale their data processing and analytics systems.

• Improve and refactor the existing backend codebase to enhance code quality and maintainability.
• Develop scalable backend systems to support complex data analysis and calculation of sustainability-related metrics such as CO2 emissions, water, and chemicals usage.
• Create automated testing frameworks for verifying data processing and analytics services across multiple client integrations.
• Prepare the backend infrastructure for deployment into production with high reliability and performance.
• Ensure the backend supports integration with external data sources and client systems efficiently.
• Achieve a significant increase in code quality as measured by industry-standard tools, moving from poor to high-ranking code status.

• Transformation and refactoring of existing code to improve structure, performance, and maintainability.
• Implementation of data pipelines for collecting, processing, and analyzing sustainability metrics such as CO2, water, and chemical usage.
• Integration of automated test suites for verifying data accuracy and processing integrity.
• Support for multiple client data inputs and service offerings within a unified backend system.
• Deployment automation and configuration management to facilitate smooth launch to production environments.

• External data sources for environmental metrics
• Client data service APIs for data ingestion
• Automated testing and CI/CD pipelines

• High code quality, aiming for top-tier ranking metrics
• Scalability to handle increasing volumes of environmental data
• Performance requirements to ensure efficient data processing
• Security protocols for sensitive environmental and client data
• Availability and reliability for production deployment

The project aims to significantly enhance the scalability, reliability, and quality of the client's sustainability data analysis platform. Expected outcomes include improved code quality with a top-tier ranking, increased processing efficiency to support larger datasets, and a robust infrastructure capable of supporting the company's growth ambitions. Although user feedback is pending, the technical improvements are projected to enable the client to better serve large enterprise clients and expand their market reach more effectively.