Monitoring: Grafana, Prometheus, ELK Stack, Great Expectations (data quality)
Communication platforms: Bloomberg Vault, Global Relay Archive, Smarsh Enterprise Archive, Microsoft Purview
Define and govern the end-to-end eComms data processing pipeline architecture: from raw ingestion through sanitisation, normalisation, de-duplication, and enrichment to ML-ready output
Design data sanitisation processes: HTML/RTF stripping, embedded image noise removal, email header cleaning, and thread delineation for reply chains
Build disclaimer detection and removal systems using pattern matching and ML classifiers — covering legal footers, confidentiality notices, and regulatory boilerplate
Develop signature block detection and extraction using structural analysis
Design whitelist management frameworks: approved counterparties, internal distribution lists, automated system message exclusions, with periodic review cycles and jurisdiction-specific separation
Design metadata enrichment workflows: desk assignment, book mapping, counterparty risk tier, jurisdiction tagging, and timestamp UTC alignment
Define and measure data quality KPIs: completeness rate, dedup accuracy, noise removal precision, signal loss rate (target < 2%), and pipeline throughput/latency SLAs
Advise the ML team on data preparation requirements for transformer models: tokenisation strategies, sequence formatting, label engineering, and data augmentation
Conduct regular pipeline quality audits and produce data quality scorecards for compliance review
Document pipeline specifications, data dictionaries, and operational runbooks for regulatory examination readiness
