AWS Lead with Data Migration Exposure

• Data Migration Development Lead (AWS).
• Project : CPQ Data Migration
• Location : St. Louis, MO (5 days onsite)

JD:

• We are seeking a Data Migration Development Lead to drive the design, development, and delivery of large-scale data migration solutions using the AWS platform. This role combines hands-on technical leadership with responsibility for migration architecture, data engineering, quality assurance, and production cutover execution.
• Key Responsibilities
• Lead end-to-end data migration design, development, and implementation.
• Build scalable ETL/ELT and migration frameworks using AWS services.
• Design data extraction, transformation, cleansing, validation, loading, and reconciliation processes.
• Develop high-volume migration pipelines using AWS Glue, S3, Lambda, Step Functions, DMS, Redshift, and RDS/Aurora.
• Implement automated data quality, audit, reconciliation, and monitoring controls.
• Support multiple migration cycles including testing, mock conversions, and production cutovers.
• Develop migration runbooks, restart/recovery procedures, and operational controls.
• Establish CI/CD pipelines and Infrastructure as Code using Terraform, CloudFormation, or AWS CDK.
• Lead code reviews, technical design reviews, and mentor migration developers.
• Collaborate with architects, business stakeholders, and application teams to ensure successful migration outcomes.
• Required Qualifications
• 8+ years of experience in data engineering, ETL/ELT, or data migration.
• 3+ years of experience leading technical teams or migration workstreams.
• Strong expertise in AWS services, Python, SQL, and PySpark.
• Experience with data quality frameworks, reconciliation, and large-scale production migrations.
• Strong knowledge of relational databases, data modeling, and performance optimization.
• Experience in Agile delivery environments with strong troubleshooting and analytical skills.
• Success Measures
• Accurate and complete data migration with minimal production defects.
• Successful cutover execution within planned timelines.
• Automated reconciliation, auditability, scalability, and reusability of migration frameworks.