Skill: Pyspark, AWS
Exp: 6 to 12 years
Location: Pune

Job Summary

Key Responsibilities

Design, develop, and maintain ETL pipelines using PySpark on AWS.

Work with large datasets to perform data cleansing, transformation, and aggregation.

Optimize Spark jobs for performance and cost efficiency.

Integrate data from multiple sources including AWS S3, RDS, Redshift, DynamoDB, and APIs.

Implement data quality checks and ensure compliance with governance policies.

Collaborate with data analysts, data scientists, and business stakeholders to deliver data solutions.

Deploy and monitor jobs using AWS Glue, EMR, Step Functions, or Lambda.

Troubleshoot and resolve issues in production data pipelines.

Maintain documentation for data flows, transformations, and architecture.

Required Skills & Qualifications

3-7 years of experience in data engineering or big data development.





Strong proficiency in PySpark and Spark SQL.

Hands-on experience with AWS services such as:
S3, Glue, EMR, Lambda, Athena, Redshift, IAM

Solid understanding of ETL concepts and data warehousing.

Proficiency in Python for scripting and automation.

Experience with version control (Git) and CI/CD pipelines.

Strong problem-solving skills and ability to work in an agile workplace.

Preferred Skills

Experience with AWS Glue Studio and Glue Catalog.

Knowledge of Airflow or other orchestration tools.

Familiarity with streaming data using Kafka or Kinesis.

Exposure to Terraform or CloudFormation for infrastructure as code.

Understanding of data security and encryption in AWS.

📌 Aws, Pyspark (Chennai)
🏢 Cognizant
📍 Chennai