Tech Stack
Python, Neo4j, Cypher, JSON.
Timeline
The internship ran from July 2024 to September 2024.
Context & Scope
During an internship at iCog Labs, my technical capabilities were evaluated through a fast-paced database integration initiative. I was tasked with designing and building a native database driver layer to bridge a Graph Database with a complex gene annotation backend system, moving from assessment to final deployment within four weeks.
Deliverables
I engineered a modular, object-oriented pipeline to handle graph queries, data parsing, and automated database initialization from scratch:
- Cypher Query Generator: Wrote a core compilation function that ingests structured JSON data (lists of nodes and predicates) and dynamically generates valid Neo4j Match statements.
- Automated Dataset Loader: Developed an initialization utility that parses local
.cypherfiles and seeds the Neo4j graph database automatically when the backend boots up. - Graph-to-JSON Output Parser: Built an extraction function that cleans and restructures raw Neo4j query returns, formatting complex graph topologies back into predictable JSON nodes and edges (predicates).
- Unified Database Engine: Packaged the query generator, loader, runner, and parser into a cohesive
CypherGeneratorclass, seamlessly registering the Neo4j engine directly inside the gene annotation backend’s database initialization array (app/init.py).
Key Outcomes & Impact
- Rapid MVP Lifecycle: Successfully architected, tested, and shipped a complete database integration wrapper inside a rigorous 4 weeks timeline.