Data Migration Development Lead _ AY
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Role details
Tech stack
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Job description
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., * 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.
Requirements
Data Migration, Data Migration, Technical Leadership, Quality Assurance, Amazon Web Services (AWS), Database Extract Transform and Load (ETL), Scalable System Development, Reconciliation, Amazon Simple Storage Service (S3), AWS Lambda, * 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.
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