The Role
Build and maintain scalable ETL/ELT pipelines, Spark and PySpark processing jobs, and AWS-based data lake and warehouse architectures. Implement data validation, partitioning, schema management, lineage, metadata, governance, monitoring, and alerting. Collaborate with analytics, BI, product, and engineering teams to ensure reliable data for reporting and insights. Use Git and CI/CD tools to manage code and automate workflow deployment.
Summary Generated by Built In
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.
- 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.
- 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.
Skills Required
- 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 S3, Glue, Lambda, Redshift, Athena, EMR, and Step Functions
- Understanding of Data Lake architecture, ETL/ELT frameworks, and data warehousing concepts
- Familiarity with Delta Lake, Spark SQL, or other big data frameworks
- Experience with data modeling and performance tuning
- 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
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The Company
What We Do
ConveGenius is an education-technology company focused on making learning accessible and engaging for every child. It develops technology-enabled education solutions, including SwiftChat, SwiftPAL, Swift Insights, and Vidya Samiksha Kendra initiatives, and collaborates with governments and partners to support educational transformation. Its work combines digital learning, AI-powered tools, and large-scale outreach, with deployments spanning Indian states and a user base exceeding 150 million.
