Build and support scalable AWS data infrastructure, lakehouse platforms, pipelines, and governance tools. Manage AWS resources including S3, Redshift, Glue, EMR, and MWAA; develop ETL workflows using SQL, Spark, and Airflow; and design data models and quality practices. Collaborate with data scientists and BI engineers, automate reporting and self-service support, improve data availability, and contribute to large-scale banking data engineering initiatives.

Leads a team of scientific data analysts supporting Reaxys, Embase, and future AI-enabled life sciences platforms. Oversees search and API quality, data discoverability, testing, feature releases, quality standards, and customer-focused insights. Uses scientific expertise, Python and Bash scripting, databases, cloud platforms, and AI-enabled testing workflows to improve data integrity and platform performance. Coordinates stakeholders, documents work, presents findings, and drives cross-functional projects.