Shammah E

Headed end-to-end development of a multi-tenant AI system, driving a 30% sales increase by delivering a differentiated product for automated market intelligence across quantum, AI, space and climate tech verticals.

Streamlined business decision-making processes for fintech clients with the design and deployment of comprehensive data warehousing and analytics solutions. Optimized data pipelines for machine learning workflows by developing production-grade AI systems to support large language models and Retrieval-Augmented Generation (RAG) applications. Accelerated graph database retrieval by 30%, by optimizing Neo4j data models, reducing response latency in AI-powered applications.

Architected and deployed cloud-native ETL/ELT pipelines on GCP and AWS, increasing downstream processing efficiency by 65%. Designed and maintained a scalable spatial data warehouse in BigQuery to streamline and support large-scale geospatial data processing, boosting data availability by 65%. Shaved cloud data storage costs 35% by implementing a cost-efficient data lake storage strategy in Google Cloud Storage (GCS) and S3. Delivered 12X acceleration to downstream analytics by leveraging distributed processing techniques and frameworks in building serverless ETL pipelines. Published the largest open-source global footprint data set (as of 2023). Led the integration of a dozen continent-scale data sets, ensuring high data integrity and quality standards, and leveraging serverless ETL workflows and compact cloud-native data formats like Parquet, PMTiles, and Cloud Optimized Geotiffs (COGS).