Scalable Data Infrastructure and Automation for Financial Data Management Platform

The client faces limitations in its technological infrastructure, including a shortage of in-house development resources, complex data management pipelines, and infrastructure inefficiencies that hinder seamless delivery, scalability, and user experience. Their data pipeline requires restructuring and optimization to handle global financial filings effectively, while existing systems lack automation, monitoring, and integration capabilities, impacting operational efficiency and reliability.

A mid-sized financial education provider that delivers online training and certification programs to top investment banks and financial institutions, focusing on financial literacy, fund management, and valuation modeling.

• Significantly increase data processing capacity to support operations for over 9,000 global companies, improving reliability and accessibility.
• Automate data extraction, classification, translation checks, and update processes to reduce manual effort and errors.
• Develop scalable ETL pipelines with enhanced monitoring, failure alerts, and metadata updates to ensure data integrity and timeliness.
• Implement secure, cloud-based deployment with CI/CD pipelines for rapid, reliable application updates.
• Expand internal development capabilities by recruiting specialized engineering talent to accelerate feature development and system improvements.
• Enhance user experience by integrating advanced reporting, certification workflows, and dashboard functionalities.
• Achieve measurable improvements in operational efficiency, data accuracy, and stakeholder trust, aiming for an NPS close to 10.

• Automated ETL pipelines for timely, reliable updates of global financial filings from third-party providers, including document duplication detection, translation verification, and classification.
• Web-based dashboard for monitoring pipeline execution, data access, and system status.
• Regular updates of company metadata with change detection mechanisms for tickers, listings, and other key attributes.
• Enhanced connection to external APIs (e.g., SEC API) with job status tracking, failure notifications, and data completeness checks.
• Development of data extraction services to capture tagged information from filings and build enterprise valuation models visualized through web applications, with export functionalities to Excel.
• Design of REST API endpoints to enable integration of valuation models, reporting tools, and data exports into existing and future systems.
• Deployment of CI/CD pipelines for containerized applications, with rigorous access controls and security policies.
• User-facing reporting modules to track interactions, generate business metrics, and support data-driven decisions.
• Integration of online certification processes to recognize user achievement and extend platform capabilities.
• Implementation of quality assurance procedures including testing protocols, regular audits, and continuous feedback loops.

• Third-party financial filings providers for automated data updates
• SEC API for company filings and metadata extraction
• External services for document translation and classification
• Online certification services for user achievement workflows

• Platform scalability to support data pipelines processing data for over 9,000 companies
• High system reliability with failure detection, instant notifications, and monitoring
• Security protocols including access control, data protection, and secure API endpoints
• Performance standards ensuring timely data processing and reporting
• DevOps practices with automated deployment pipelines and environment consistency

The implementation of a scalable, automated data management platform is expected to significantly boost operational efficiency, enabling data processing for over 9,000 companies. The automation of data extraction and updates will reduce manual workload, improve data accuracy, and enhance stakeholder trust. Internal team expansion and improved system reliability are projected to increase project throughput by 15%, leading to faster feature iteration and business growth. Overall, the project aims to deliver a Net Promoter Score close to 10, reflecting superior user satisfaction and platform performance.