Junior Data Engineer

ConveGenius.AI; Shimla district; On-site; INR 800,000 - 1,200,000; Full time

Job summary

ConveGenius is seeking a Junior Data Engineer to join its Data Platform team in India. You will design and maintain scalable data pipelines and architectures using AWS services, enabling reliable data movement, transformation, and analytics for millions of students and schools.

You will collaborate with analytics, product, and engineering teams to support reporting, dashboards, and insights, and you will work with Spark, PySpark, SQL, and Data Lake/ warehouse technologies to ensure data quality

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Data Engineering, or a related field.
• 3-5 years of hands-on experience in data engineering, pipeline development, or cloud-based data systems.
• Strong knowledge of SQL and Python/PySpark.
• Practical experience with AWS services (S3, Glue, Lambda, Redshift, Athena, EMR, Step Functions).
• Solid understanding of Data Lake architecture, ETL/ELT frameworks, and data warehousing concepts.
• Familiarity with Delta Lake, Spark SQL, or other big data frameworks, along with data modeling and performance tuning.

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.

Skills

SQL

Python/PySpark

Spark

ETL/ELT pipelines

Data modeling

Data warehousing

Delta Lake

AWS

Spark SQL

GCP/Azure (nice-to-have)

Databricks

PostgreSQL

MySQL

NoSQL

Airflow

Education

Bachelor's degree in CS/IT/Data Engineering

Tools

S3

Glue

Lambda

Redshift

Athena

EMR

Step Functions

Delta Lake

Job description

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.