I build reliable data platforms on AWS.

Project senior lead across ETL, Reporting, Analytics and Infrastructure.

• Set technical direction and coordinate delivery across the project’s data and infrastructure workstreams.
• Designed an event-driven AWS platform spanning on-premises ingestion, bronze and silver processing, analytics base tables, model-refresh triggers and dashboards, with auditing and monitoring throughout.
• Re-engineered AWS Glue and PySpark workloads to reduce measured runtime by 70%.
• Set daily priorities, cleared delivery blockers and helped the team complete 30 use cases within the agreed timeline.

AWS migration, governance and data-quality engineering for financial-services programs.

• Designed full-load and incremental pipelines that moved 10+ TB from Oracle and 30 TB from a SAS warehouse to Amazon S3 in Parquet format, reducing the storage bill by 65%.
• Built deterministic PII-masking pipelines in Glue and PySpark, preserving referential integrity and enforcing role-based access through Lake Formation.
• Developed metadata-driven reconciliation across Oracle, S3, RDS and Redshift, using Aurora PostgreSQL rules and consolidated validation outputs.
• Orchestrated ingestion, masking and reconciliation with Step Functions, Lambda, Glue and SNS, including dependency handling and automated alerts.

AWS modernisation and analytics automation for a US financial-services program.

• Built Python ingestion and transformation services across EC2, Lambda and S3, with batch orchestration in Amazon MWAA and Airflow.
• Developed production Airflow DAGs with deployment support, data-quality checks and failure alerting.
• Automated same-account and cross-account QuickSight asset migration with reusable Python and Boto3 tooling.
• Built Python and SQL reconciliation tooling for schema and row-level validation, achieving a measured 90x improvement over the previous process.