20 engineering case studies across data, AI, automation and analytics, each with the problem, the architecture, and the numbers that moved.
From a 5-hour manual backlog to sub-15-minute automated quotes
A two-phase automation for an aerospace-logistics supplier that replaced manual RFQ handling and rolling spreadsheets with Dagster orchestration plus…
The same AWS data-engineering playbook, applied to the realities of each industry and backed by the case studies above.
Data warehouse migrations, regulatory reporting and risk analytics for retail banks, payments processors and financial-services groups. Recent work includes a tier-1 bank Sybase-to-Redshift migration and a delta-lake platform that unlocked $2M+ in revenue.
Lakehouse modernisation and operational analytics for national water, wastewater and energy utilities, cutting data-platform operating cost in half while letting analytics scale to demand.
Pipeline and RFQ-operations automation for aerospace-logistics and parts suppliers, turning a five-hour manual quoting backlog into sub-15-minute automated quotes on AWS.
Distributor sales-and-stock ETL, data homologation and real-time analytics for FMCG and retail brands, including hands-off SFTP-to-analytics pipelines for Unilever distributors.
Big-data finance platforms and reporting for telecom operators: Cloudera, Spark and Hive workflows that cut financial-reporting errors by 50%.
Document intelligence, multilingual OCR and NLP analytics for healthcare, government digitisation and research, from automated form extraction to mining 50M+ posts for public-health signal.
From messy source data to analytics-ready warehouses that cut cost. Let's scope it. I reply within one business day.