Key Responsibilities

- Develop and maintain data pipelines using PySpark and AWS Glue.
- Build serverless data processing solutions using AWS Lambda.
- Design and optimize data storage and analytics solutions on S3, Athena, Redshift, and DynamoDB.
- Implement ETL/ELT processes for data ingestion and transformation.
- Ensure data quality, performance, and reliability across data platforms.
- Collaborate with cross-functional teams to understand and deliver business requirements.
- Monitor, troubleshoot, and support production data workflows.

Required Skills

- Strong experience with Python and PySpark.
- Solid expertise of optimizations
- Hands-on experience with AWS Glue, Lambda, S3, Athena, Redshift, and DynamoDB.
- Strong SQL and data analysis skills.
- Experience building and optimizing large-scale data pipelines.
- Good understanding of data warehousing and data lake concepts.

Good to Have

- Experience with Git and CI/CD pipelines.
- Knowledge of Infrastructure as Code (Terraform or CloudFormation).
- Experience with data quality and data governance frameworks.
- AWS certifications.
- Exposure to real-time or event-driven data processing solutions.

📌 AWS Data Engineer (Hyderabad)
🏢 Paltech
📍 Hyderabad