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Thuc Duong
Senior Data Engineer specializing in AI-driven GTM analytics and LLM evaluation
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About
Data/analytics engineer who stood up foundational pipelines and services at Meta for the Ray-Ban Meta launch—building a retailer sales ingestion system (S3/Hive) with rigorous DQ checks, 1-day SLAs, and dimensional rollups used by GTM to track sales trends. Also built a modular multi-retailer web-scraping system for out-of-stock alerts and shipped internal GraphQL APIs and an n8n-like workflow builder using serverless (AWS Lambda) with strong testing and observability practices.
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Key Strengths
- Built end-to-end sales data pipeline from scratch (S3 → Hive staging → DQ checks → fact/dimension enrichment → cumulative serving) at ~10M rows
- Strong data quality engineering (uniqueness, monotonicity, non-null, type/abnormal value checks) and auditability via append-only raw tables
- Reliability ownership via strict 1-day SLAs and proactive capacity planning for peak periods
- Stakeholder-driven scoping: synthesizes competing metric/dimension requests into themes and communicates tradeoffs clearly
- Designed modular multi-retailer web scraping system with per-retailer parsers, extensive logging, and safe schema evolution/deprecation
- Shipped internal GraphQL API with golden-dataset unit tests, backward-compatible field deprecation, and serverless scaling on AWS Lambda
- Delivered an early-stage full-stack internal workflow builder (n8n-like) with pragmatic architecture choices and production monitoring
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