About the Role :

We are looking for an experienced AWS Data Engineer to design, build, and maintain scalable data pipelines and data processing solutions on AWS. The ideal candidate should have strong hands-on experience with AWS Glue, Python, PySpark, Lambda, Snowflake, DBT, and SQL, along with a good understanding of modern data engineering practices.

Key Responsibilities :

- Design, develop, and maintain scalable and reliable data pipelines using AWS services.

- Build and optimize ETL/ELT workflows using AWS Glue, Python, and PySpark.

- Develop serverless data processing and automation solutions using AWS Lambda.

- Work with Snowflake for data storage, transformation, and analytics workloads.

- Develop and manage data transformation workflows using DBT.

- Write complex and optimized SQL queries for data extraction, transformation, and analysis.

- Perform data cleansing, validation, transformation, and integration from multiple sources.

- Optimize data pipelines for performance, scalability, reliability, and cost efficiency.

- Collaborate with Data Analysts, Data Scientists, Architects, and other engineering teams to understand data requirements.

- Troubleshoot data pipeline failures and resolve data quality and performance issues.

- Follow best practices around data security, governance, monitoring, and documentation.

Tech Stack & Skills :

- AWS Data Engineering, AWS Glue, Python, PySpark, AWS Lambda, Snowflake, DBT, SQL, ETL/ELT, Data Warehousing.

Good to Have :

- Experience with additional AWS services such as S3, Athena, Redshift, CloudWatch, or Step Functions.

- Knowledge of CI/CD and version-control practices using Git.

- Understanding of data quality, monitoring, and governance frameworks.

- Experience working in Agile/Scrum environments.