Senior Cloud Data Engineer

## About the role This is a chance to work at the center of a complex and consequential cloud transformation for a major financial services org. We are looking for sharp, delivery-focused data consultants to lead the migration of a large data platform from Google Cloud Platform to AWS. You will not be reviewing architecture diagrams from the sidelines; you will be building the pipelines, designing the schemas, and making the hard calls that keep petabytes of critical financial data moving accurately and on schedule. If you thrive in technically demanding environments, love solving gnarly data problems, and want your fingerprints on an enterprise-scale transformation, this engagement is for you. ## What You Will Do - Own end-to-end data migration execution: Drive the full OLAP/OLTP migration from GCP to AWS: data mapping, schema conversion, and hands-on lift-and-shift execution. - Build rock-solid synchronization pipelines: Design and implement data sync pipelines with tight SLAs for latency, consistency, and error recovery. Zero ambiguity on failure modes. - Bring the target architecture to life: Implement the AWS data architecture end to end, including driver personalization engines and production-grade dashboarding solutions. - Migrate historical and time-series data at scale: Execute large-volume historical data migrations with rigorous integrity checks and minimal disruption to live operations. - Build observability from the ground up: Create monitoring, alerting, and reconciliation frameworks that give the team real-time confidence in cross-cloud data fidelity. - Elevate the team around you: Share your expertise freely. Mentor peers, unblock blockers, and raise the technical floor of the whole engagement. - Partner closely with the client: Work shoulder-to-shoulder with stakeholders to nail data governance, access patterns, and analytics requirements. ## What You Bring ### Data Migration - BigQuery to Redshift, Cloud SQL to Aurora/RDS: you have done this before and have the scars to prove it - Data mapping, schema conversion, and ETL/ELT pipeline design at enterprise scale ### OLAP and OLTP Systems - Dimensional modeling: star and snowflake schemas, done right - Transactional database optimization and time-series data migration ### Data Synchronization - CDC (Change Data Capture), real-time replication, latency tuning - Strong opinions on eventual vs. strong consistency, and when each applies - Battle-tested error handling and retry strategies ### AWS Data Services - Redshift, Aurora PostgreSQL, DMS, Glue, Kinesis, MSK (Kafka), Lambda, S3, Lake Formation ### Analytics and Personalization - Amazon QuickSight, Grafana, dashboarding frameworks, driver personalization engines ### GCP - BigQuery, Cloud Spanner, Dataflow, Pub/Sub, Cloud SQL: you know the terrain you are leaving ### Infrastructure as Code and CI/CD - Terraform, CloudFormation, and automated CI/CD for data pipelines ### Certifications - Cloud certifications are a plus but are not required. Any of the following, or equivalent credentials from a major cloud provider, are valued: - AWS Certified Solutions Architect (Associate or Professional) - AWS Certified Data Analytics Specialty or Database Specialty - Google Professional Cloud Architect or Cloud Data Engineer