About the role

AWS Data Engineer

Job Summary:

We are seeking 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 will bring strong expertise in Python, PySpark, SQL, and AWS data services to build robust ETL/ELT pipelines that drive impactful data initiatives within our organization.

Key Responsibilities:

Design, develop, and maintain scalable data pipelines and ETL/ELT workflows on AWS.
Develop data processing solutions using Python and PySpark to ensure effective data handling.
Build and optimize data pipelines using AWS Glue ETL and Glue Data Catalog for efficient data management.
Work with Amazon S3 for data storage, ingestion, and implementing data lake solutions.
Develop serverless data processing and integration solutions using AWS Lambda.
Implement event-driven data workflows using Amazon EventBridge to enhance system responsiveness.
Design and orchestrate complex data workflows using AWS Step Functions for streamlined operations.
Utilize Amazon Athena for querying and analyzing data stored in Amazon S3 to derive actionable insights.
Write complex and optimized SQL queries for effective data extraction, transformation, validation, and analysis.
Ensure data quality, accuracy, reliability, and performance across data pipelines.
Troubleshoot pipeline failures and optimize data processing jobs for performance and scalability.
Collaborate with data architects, analysts, developers, and business stakeholders to understand and gather data requirements.
Follow best practices for AWS security, monitoring, logging, deployment, and data governance.

Requirements:

6–8 years of experience in Data Engineering with a proven track record.
Strong hands-on experience with Python, PySpark, and SQL for data processing.
Extensive experience working with AWS Cloud services and data solutions.
Hands-on experience with Amazon S3, AWS Glue ETL, AWS Glue Data Catalog, AWS Lambda, Amazon EventBridge, AWS Step Functions, and Amazon Athena.
Good understanding of Data Lake architectures, ETL/ELT processes, and data pipeline structures.
Strong knowledge of data processing, transformation, and optimization techniques.
Excellent problem-solving and troubleshooting skills.

Preferred Qualifications:

Experience with AWS data lake and serverless architectures is a plus.
Familiarity with CI/CD and DevOps practices relevant to data engineering.
Experience with cloud-based data warehousing or analytics platforms.
Understanding of data security, governance, and monitoring protocols on AWS.

Benefits:

Competitive salary and performance-based bonuses.
Comprehensive health, dental, and vision insurance packages.
Flexible work schedules and a hybrid work model.
Opportunities for professional development and continuous learning.
Generous paid time off and holiday policies.
Employee wellness programs and resources.
Collaborative and inclusive company culture focused on innovation and excellence.