Description

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

Responsibilities

• Build and optimize data ingestion frameworks using AWS Glue, Lambda, Step Functions, EMR, Kinesis, or equivalent services.
• Develop high-quality, reusable data components using Python or PySpark.
• Design data models and data flow solutions by collaborating 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: AWS Glue, AWS Lambda, AWS S3, AWS EMR, AWS Step Functions, AWS Redshift / Aurora / 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 (Airflow, Step Functions, or similar).
• Familiarity with CI/CD tools (GitHub Actions, Jenkins, CodePipeline).
• Knowledge of data testing, data validation, and quality frameworks.
• Excellent communication, documentation, and stakeholder management skills.

Key Skills

Education

Any Gradute