About the Role:

We are looking for an experienced AWS Data Engineer with 6 - 8 years of hands-on experience in designing, developing, and maintaining scalable data engineering solutions on AWS. The ideal candidate should have strong expertise in Python, PySpark, SQL, and AWS data services, with experience building robust ETL pipelines and Cloud-based data workflows.

Key Responsibilities:

- Design, develop, and maintain scalable data pipelines and ETL workflows on AWS.

- Develop efficient data processing solutions using python and pySpark.

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

- Build and manage ETL pipelines using AWS Glue.

- Work with AWS Glue Data Catalog for metadata management and data discovery.

- Design and implement serverless data processing solutions using AWS Lambda.

- Use Amazon S3 for scalable data storage and data lake solutions.

- Orchestrate and automate workflows using AWS Step Functions.

- Implement event-driven architectures using Amazon EventBridge.

- Use Amazon Athena for querying and analyzing data stored in Amazon S3.

- Monitor, troubleshoot, and optimize data pipelines for performance, reliability, and scalability.

- Collaborate with data architects, analysts, developers, and business stakeholders to understand data requirements.

- Ensure data quality, security, governance, and best practices across data engineering solutions.

Tech Stack:

- Python, PySpark, SQL, AWS Glue, AWS Glue Data Catalog, AWS Lambda, Amazon S3, AWS Step Functions, Amazon EventBridge, Amazon Athena.

Preferred Skills:

- Experience working with AWS Data Lake architectures.

- Knowledge of data partitioning, file formats such as Parquet/Avro, and data optimization techniques.

- Experience with CI/CD and DevOps practices for data pipelines.

- Understanding of data security, IAM, monitoring, and governance on AWS.

- Good problem-solving and communication skills.

Candidate Requirements:

- Notice Period: Immediate to 15 Days.

- Experience: 6 - 8 Years.

- Work Mode: Hybrid.