Description

You will design, build, and maintain scalable ETL/ELT data pipelines on AWS.

This role is on-site.

Responsibilities

• Build and optimize data ingestion frameworks using AWS Glue, Lambda, Step Functions, EMR, and Kinesis.
• Develop high-quality, reusable data components using Python and PySpark.
• Design data models and data flow solutions in collaboration with architects and product teams.
• Implement data quality checks, validation rules, and automated monitoring frameworks.
• Optimize data pipelines for performance, scalability, and cost efficiency.

Required Skills

• 7+ years of experience in data engineering or big data development.
• Strong hands-on experience with AWS data services: Glue, Lambda, S3, EMR, Step Functions, Redshift, Aurora, or DynamoDB.
• Proficiency in Python, PySpark, and distributed data processing.
• Experience with Spark, dataframes, and performance tuning.
• Strong understanding of ETL/ELT design patterns and data modeling.
• Experience with data orchestration tools such as Airflow or Step Functions.
• Familiarity with CI/CD tools like GitHub Actions, Jenkins, or CodePipeline.
• Knowledge of data testing, validation, and quality frameworks.

Preferred Skills

• Excellent communication, documentation, and stakeholder management skills.

Key Skills

Education

Any Gradute