AWS Data Engineer

About the Role

We are looking for a Senior AWS Data Engineer to join our team and lead the modernization of a large-scale cloud data platform. You will be responsible for designing, building, and optimizing scalable data pipelines, modernizing ETL/ELT workloads, implementing data governance, and enabling analytics and reporting solutions on AWS.

The ideal candidate has deep expertise in AWS data services, data engineering, pipeline orchestration, and data warehouse modernization, with a passion for building reliable, secure, and scalable data platforms.

Location: Remote
Experience Required: 5+ Years
Employment Type: Contract

Key Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL/ELT workflows on AWS.
• Modernize legacy data platforms by migrating Apache Airflow/MWAA workloads to AWS-native orchestration services.
• Migrate and optimize Amazon EMR workloads to AWS Glue.
• Design and implement scalable data lake and data warehouse solutions using AWS services.
• Develop robust data models, semantic layers, and standardized KPI definitions to support business analytics.
• Build and maintain data quality, validation, anomaly detection, and monitoring frameworks.
• Implement metadata management, data lineage, and data governance best practices.
• Develop automated deployment pipelines and infrastructure using Infrastructure as Code (IaC).
• Optimize data pipelines for performance, scalability, reliability, and cost efficiency.
• Collaborate with business stakeholders to understand reporting requirements and translate them into scalable data solutions.
• Support QuickSight semantic datasets, dashboard optimization, and Power BI migration initiatives.
• Participate in UAT, production deployments, knowledge transfer, and post-production support.

Required Skills & Qualifications

• 5+ years of experience in Data Engineering.
• Strong programming skills in Python and SQL.
• Hands-on experience with Apache Spark / PySpark.
• Extensive experience with AWS data services including:

1. AWS Glue
2. Amazon S3
3. Amazon Redshift
4. Amazon Athena
5. AWS Lambda
6. AWS Step Functions
7. Amazon EventBridge
8. AWS IAM

• Experience designing and implementing ETL/ELT pipelines.
• Strong knowledge of data modeling, data warehousing, and data lake architecture.
• Experience with Apache Airflow or Amazon MWAA.
• Strong understanding of Data Governance, Metadata Management, Business Glossary, and Data Lineage.
• Experience implementing Data Quality, Audit Logging, and Data Reconciliation frameworks.
• Experience with Amazon QuickSight, semantic datasets, and BI reporting.
• Experience with Git, CI/CD, and Infrastructure as Code (Terraform or CloudFormation).
• Excellent analytical, troubleshooting, and communication skills.

Preferred Qualifications

• Experience with Power BI migration projects.
• Experience working with Healthcare or Life Sciences data platforms.
• Knowledge of Lake Formation, AWS Glue Data Catalog, and AWS security best practices.
• Familiarity with ML-ready data pipelines and feature engineering.
• AWS Certification (Data Engineer, Solutions Architect, or Developer) is a plus.