High-Level Overview#

This project focused on building and maintaining a broad set of integrations between the Knowledge Base platform and external client systems. The goal was to reliably exchange large volumes of heterogeneous academic data while minimizing integration-specific complexity and long-term maintenance costs.

Over time, the scope evolved from implementing individual point-to-point integrations into designing a reusable, standardized integration framework used across the product.

Key Integrations#

As part of this work, I implemented and maintained multiple production integrations, including:

• Embeddable widgets allowing clients to display Knowledge Base content directly on their own websites while linking back to authoritative records.
• OAI-PMH interface compliant with the Open Archives Initiative Protocol for Metadata Harvesting, enabling external systems to harvest metadata from the Knowledge Base.
• Web of Science integration - my first integration project at the company, implemented end-to-end during my first month as a trainee.
• Scopus integration, including migration and synchronization of bibliometric indicators.
• USOS integration for importing theses, authors, employees, and related academic entities.

Standardized ETL Framework#

While working on multiple integrations, I identified significant duplication in messaging, job orchestration, and error-handling logic. To address this, I designed and implemented a shared integration library that effectively became a lightweight ETL framework for the platform.

The standardized pipeline consists of the following stages:

1. Extraction - retrieving data from client APIs or databases.
2. Transformation - mapping external data into an internal, intermediate domain model.
3. Validation - verifying data correctness and completeness, with support for skipping invalid records or signaling errors.
4. Load - persisting validated data into the Knowledge Base.

With this approach, new integrations require only:

• Definition of the intermediate validation model.
• Implementation of mapping logic between the client’s data model and the Knowledge Base domain model.

All remaining concerns - messaging, retries, error handling, batching, and execution flow - are handled by the shared framework.

Key Outcomes#

• Drastically reduced boilerplate code across integrations.
• Significantly shortened development time for new client integrations.
• Improved consistency, reliability, and observability of ETL processes.
• Established a scalable integration pattern reused across the product.

This project demonstrates my ability to move beyond feature delivery and identify structural improvements that increase team productivity and system maintainability at scale.