AWS Senior Data Engineer

Our customer provides innovative solutions and insights that enable our clients to manage risk and hire the best talent. Their advanced global technology platform supports fully scalable, configurable screening programs that meet the unique needs of over 33,000 clients worldwide. Headquartered in Atlanta, GA, they have an internationally distributed workforce spanning 19 countries with about 5,500 employees. Our partner perform over 93 million screens annually in over 200 countries and territories.

We are seeking a Senior Data Engineer with strong database expertise (SQL, data modeling, query optimization), extensive AWS data services experience, and proven ETL development capabilities to join our Data Engineering Team and help us build a robust, scalable Data Analytics Platform leveraging AWS cloud infrastructure.

• Develop reusable, metadata-driven data pipelines with strong focus on database design and SQL optimization
• Work extensively with AWS-based datasets and data infrastructure including Redshift, RDS, S3, Glue, Athena, and DMS
• Support comprehensive ETL development and optimization using advanced SQL techniques and query performance tuning
• Automate and optimize data platform processes leveraging AWS data services ecosystem
• Build robust integrations with data sources and consumers using AWS native solutions
• Proactively resolve performance and data quality issues through SQL optimization and database tuning
• Leverage DBT for data transformation modeling and Jenkins/GitHub Actions for CI/CD automation
• Contribute to platform documentation and runbooks with focus on database best practices
• Propose and implement improvements to data platform architecture using AWS data services

• Strong database expertise: advanced SQL skills, comprehensive data modeling experience, and proven query optimization capabilities
• AWS data services proficiency: hands-on experience with Redshift, RDS, S3, Glue, Athena, and DMS
• ETL development: strong background in ETL development and optimization using database-centric approaches
• Programming: Python/PySpark and advanced SQL for data pipeline development
• Spark proficiency: expert-level skills in building data pipelines using Databricks on AWS or EMR/EMR Serverless
• Large-scale data processing: extensive experience working with complex datasets and database performance optimization
• Library development: skilled in building reusable data transformation modules as Python packages
• CI/CD expertise: experience with GitHub Actions, Jenkins, and AWS CodePipeline for automated deployments
• AWS networking/security: Understanding of VPC, subnets, security groups, and IAM
• Agile methodology: familiar with Scrum development practices

• DBT proficiency: experience with DBT for data transformation and modeling
• Jenkins/GitHub Actions: advanced automation capabilities beyond basic CI/CD
• Experience with Infrastructure as Code (Terraform, AWS CloudFormation)
• Knowledge of AWS-native data services ecosystem (Glue, Lambda, S3, Redshift, RDS, Athena)