Our scale-up higher education company is seeking a skilled data engineering team to implement a real-time data mesh architecture. This initiative aims to enhance our ability to analyze diverse student success metrics across departments, enabling data-driven decision-making and personalized student support. The project involves integrating Apache Kafka, Spark, and Airflow to create a robust data pipeline with Databricks and Snowflake for storage and analytics.
University departments focused on student retention, academic advisors, and institutional researchers who require real-time insights into student performance and engagement data.
The current data infrastructure is fragmented and inefficient, limiting our ability to provide timely and accurate insights into student performance, which is critical for improving retention and success rates.
A data-driven approach is increasingly mandated by educational standards and competitive pressures, pushing institutions to invest in advanced analytics for better student outcomes and institutional rankings.
Failure to address data fragmentation will result in continued inefficiencies, reduced student engagement, lower retention rates, and diminished competitive positioning in the education market.
Many institutions rely on traditional data warehouses and periodic batch processing, which lack the agility and real-time capabilities necessary for today’s dynamic educational environments.
Our approach focuses on real-time analytics via a data mesh, empowering faculties with immediate insights and fostering a culture of data-driven decision-making across all levels of the institution.
Our strategy will involve targeted demonstrations of our enhanced analytics capabilities at educational conferences and workshops, alongside partnerships with academic networks to showcase data-driven student success stories.