Junior Data Engineer

Convegenius Edu; Shimla district; Vor Ort; INR 600.000 - 900.000; Vollzeit

Zusammenfassung

Convegenius Edu is seeking a Junior Data Engineer to design and maintain scalable data pipelines and architectures using AWS services, enabling reliable data movement, transformation, and analytics for millions of students and schools in India.

You will collaborate with analytics, product, and engineering teams to support reporting, dashboards, and insights, while applying best practices in data lake, data warehouse, and governance.

Qualifikationen

• Bachelor's degree in CS/IT/Data Engineering or related field.
• 35 years of hands-on experience in data engineering or cloud-based data systems.
• Strong knowledge of SQL and Python/PySpark.
• Practical experience with AWS (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, and data modeling/performance tuning.

Aufgaben

• 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 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 queries.
• 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.

Kenntnisse

SQL

Python/PySpark

ETL/ELT pipelines

Ausbildung

Bachelor's degree in CS/IT/Data Eng

Tools

AWS stack (S3, Glue, Lambda, Redshift, Athena, EMR)

Jobbeschreibung

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: 35 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.