Senior Cloud Data Engineer

Kunai, part of the PwC Network, builds full-stack technology solutions for financial services. They are seeking a Senior Cloud Data Engineer to lead the migration of a large data platform from Google Cloud Platform to AWS, involving hands-on execution and collaboration with clients.

Responsibilities:

• 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

Requirements:

• Bachelor's Degree, in lieu of a degree, demonstrating in addition to the minimum years of experience required for the role, three years of specialized training and/or progressively responsible work experience in technology for each missing year of college is required
• 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
• Dimensional modeling: star and snowflake schemas, done right
• Transactional database optimization and time-series data migration
• 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
• Redshift, Aurora PostgreSQL, DMS, Glue, Kinesis, MSK (Kafka), Lambda, S3, Lake Formation
• Amazon QuickSight, Grafana, dashboarding frameworks, driver personalization engines
• BigQuery, Cloud Spanner, Dataflow, Pub/Sub, Cloud SQL: you know the terrain you are leaving
• Terraform, CloudFormation, and automated CI/CD for data pipelines
• 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