Junior Data Engineer

About ConveGenius
ConveGenius is an AI-native education infrastructure company transforming how governments, enterprises, and social institutions deliver learning and governance outcomes at population scale. Operating across 22+ Indian states, ConveGenius works across B2G and B2B segments through its two core business lines — SwiftAI (AI, automation, and governance intelligence) and SwiftSchool (adaptive learning, digital classrooms, and analytics).
Role Overview
We are looking for a Junior Data Engineer to join our Data Platform team. You will design and maintain scalable data pipelines and architectures using AWS services, enabling reliable data movement, transformation, and analytics at scale. You will collaborate with analytics, product, and engineering teams to support reporting, dashboards, and insights for millions of students and schools.
Key Responsibilities
Design, build, and maintain ETL/ELT pipelines for large-scale data ingestion, transformation, and loading.
Develop and optimize Spark and PySpark jobs for batch and real-time data processing.
Work with AWS services (S3, Glue, Lambda, Redshift, Athena, EMR) to manage the data ecosystem.
Support the design and implementation of Data Lake and Data Warehouse architectures.
Implement data validation, partitioning, and schema management for efficient query performance.
Collaborate with data analysts and BI teams to ensure data availability and consistency.
Maintain data lineage, metadata, data quality, and governance.
Implement monitoring and alerting for data pipelines.
Use Git and CI/CD tools to manage code and automate deployment of data workflows.
Qualifications
Education: Bachelor's degree in Computer Science, Information Technology, Data Engineering, or a related field.
Experience: 3–5 years of hands-on experience in data engineering, pipeline development, or cloud-based data systems.
Core Skills: Strong knowledge of SQL and Python/PySpark.
Cloud & Data Stack: Practical experience with AWS (S3, Glue, Lambda, Redshift, Athena, EMR, Step Functions).
Architecture: Solid understanding of Data Lake architecture, ETL/ELT frameworks, and data warehousing concepts.
Frameworks: Familiarity with Delta Lake, Spark SQL, or other big data frameworks, along with data modeling and performance tuning.
Nice to Have
Exposure to GCP (BigQuery, Dataflow) or Azure (Data Factory, Synapse, Databricks).
Experience with Databricks, PostgreSQL, MySQL, NoSQL, or Airflow.
Understanding of DevOps practices, CI/CD pipelines, and infrastructure automation.
Prior experience in an EdTech or public data ecosystem.

Work arrangement
No