Identity Verification Data Platform Modernization

Overview

The project involved modernizing a large-scale data processing platform used for identity validation, fraud detection, and analytical reporting. The system ingested data from external service providers and transformed it into reliable metrics for BI dashboards. A key part of the initiative was migrating the platform from Delta Lake to Apache Iceberg while preserving performance, stability, and cost efficiency. To reduce migration risk, a temporary dual-stack architecture was introduced, allowing Delta and Iceberg pipelines to run in parallel during the transition.

Achievements

Led the migration from Delta Lake to Apache Iceberg while maintaining schema compatibility, partitioning consistency, and metadata reliability. Improved effective cluster resource utilization by 60% while keeping the solution cost-efficient. Tuned Spark execution and Iceberg configurations to achieve comparable or better performance than the legacy implementation.

Responsibilities

• Designed and implemented PySpark-based pipelines for generating aggregated identity verification metrics for BI dashboards.
• Built initial prototype pipelines using Athena and EventBridge before migrating the logic to production-grade Spark jobs.
• Implemented a Scala-based dual-writer architecture to support parallel Delta Lake and Apache Iceberg writes during the migration phase.
• Led the Delta Lake to Apache Iceberg migration, ensuring schema compatibility, partition strategy alignment, and metadata consistency.
• Tuned Spark configurations and execution logic to improve Iceberg pipeline efficiency.
• Integrated pipelines into Airflow DAGs and managed infrastructure changes using Terraform.
• Worked across mixed Python and Scala codebases, including maintenance and extension of legacy Scala modules.

Technologies Used