Kim L

Designed and built an automated data-quality framework comparing source-system events against warehouse data and surfacing discrepancies via alerts, reducing discrepancies from ~50% to near zero within six months. Diagnosed and fixed a silent data-loss bug for hierarchical objects, reconstructing correct state without a full reload. Implemented a safe, lag-aware object-deletion process to prevent a potential customer-impacting incident. Re-architected a reporting layer to improve performance via stored procedures with predicate push-down, added row-level security, and created reusable period-over-period comparison logic. Introduced Terraform-based infrastructure-as-code for Snowflake, including a local sandbox for safe change testing, replacing manual drift-prone deployments.

Rapidly ramped up on an iPaaS integration platform to build automated data workflows for lead generation, connecting HubSpot, AWS AI services, and the ChatGPT API. Used JSONata for JSON-based transformation steps and collaborated with marketing stakeholders to iterate prompt-engineering-based integration logic aligned to business objectives.

Led a team of 20 data engineering professionals across UK and offshore India squads, delivering well-modelled BigQuery data objects for reporting, analytics, and data science. Built a high-performing team culture through trust and structured process improvement, including new integration testing practices and parallelised delivery steps. Coordinated delivery across multiple stakeholders and downstream consuming teams within a complex, multi-site programme, and supported knowledge transfer to help teams use data objects effectively.