Project

Data Pipeline for Higher Education

Built robust ETL pipelines and validation framework to ensure high-quality data ingestion across university systems, cutting processing time by 40% and scaling analytics reliability.

Overview

Designed and implemented a scalable ETL pipeline and data validation framework to support student activity analytics by integrating data from diverse university systems and in-app interactions. The solution ensured high data accuracy, reduced latency, and enabled proactive insights for student retention strategies.

Technical details

This project involved developing resilient data pipelines, automating data validation, and optimizing performance across two data storage systems: PostgreSQL and BigQuery.

  • Python (PyTest, Pydantic, Pandera) for validation framework
  • Batch processing and parallel ingestion using Python and AWS ECS
  • PostgreSQL for operational data storage
  • BigQuery for large-scale analytics
  • S3-based CSV ingestion and SSH-tunneled SQL extractors
  • DataDog for monitoring and performance tracing

Challenges

  • Integrating data from fragmented and inconsistent sources (APIs, CSVs, SQL databases)
  • Ensuring end-to-end data accuracy across Extract, Transform, and Load stages
  • Maintaining pipeline performance as data volumes grew with each new university
  • Implementing testable, maintainable, and observable ETL processes

What changed.

  • Reduced ETL pipeline processing time by 40%, enabling faster reporting and analytics
  • Improved data integrity with automated schema validation and layered error checks
  • Enabled product and data teams to generate high-confidence insights for the universities using the product
  • Scaled infrastructure to support growing volumes of multi-tenant usage data

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