Student Data Engineering– Network Insights Platform

About the Role

Join our Network Insight (NI) development team working on a large-scale data processing and analytics platform used to analyze the physical, logical, and performance layers of the network. You’ll contribute to developing data pipelines, KPI analytics, and visualization components while gaining hands-on experience with real production data engineering and backend development.

What You’ll Work On

Data Engineering & Backend Development

Develop and maintain data collection components (MongoDB, MariaDB, CSV/JSON/XML)
Implement ETL/ELT logic and KPI calculations in Python and SQL
Work with data warehouse structures and repository patterns

Pipeline Orchestration & Monitoring

Develop and maintain Apache Airflow DAGs for scheduling and orchestration

Analytics & Visualization

Contribute to Apache Superset dashboards and reports
Develop new metrics and insights across physical, logical, and performance layers
Support users in building custom queries and visualizations

What We’re Looking For

Required:

Currently pursuing a degree in Computer Science, Software Engineering, Information Systems Engineering, Industrial Engineering & Management, or a related field
programming fundamentals and experience with Python
Basic understanding of SQL and relational databases
Familiarity with Linux environments
Curiosity and eagerness to learn new data engineering and analytics technologies

Nice to Have:

Experience with data processing or analytics projects
Knowledge of MongoDB, MariaDB, or other databases
Familiarity with Apache Airflow or similar orchestrators
Experience with Python data libraries (Pandas, NumPy)
Understanding of networking concepts (OTN, L2/L3 services, tunnels, KPIs)

What You’ll Learn

End‑to‑end data engineering using Python, SQL, and Airflow
Large-scale data ingestion, transformation, and KPI computation
Working with structured and unstructured datasets across multiple DBs
Data warehouse modeling and query optimization
Building dashboards and analytical views using Apache Superset
Handling production pipelines and debugging distributed systems
Integrating AI‑driven capabilities for analytics and automation

Responsibilities

Implement features across data pipelines, transformations, and analytics components
Write clean, maintainable Python and SQL code
Create and maintain charts, dashboards, and visual insights in Superset

Please Note:

'All qualified applicants will receive consideration for employment without regard to race, age, sex, color, religion, sexual orientation, gender identity, national origin, protected veteran status, on the basis of disability, or other characteristic protected by applicable law.'