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Patrick Lee

@patricklee2

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Senior Data Engineer building scalable data platforms for AI, personalization, and analytics.

United States

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What I'm looking for

I’m looking to build trusted, scalable data platforms—partnering with Product and ML/DS on AI-powered discovery and experimentation, with strong ownership of data quality, streaming/batch pipelines, and production analytics.

I’m a Senior Data Engineer with 10+ years of experience building reliable, scalable data systems for product, AI, personalization, and analytics teams at Spotify, NTT DATA, and Amazon. I turn complex product goals into trusted data platforms—delivering faster pipelines, cleaner metrics, and better decision-making.

I partner closely with Product, Data Science, ML, and Engineering to support AI-powered discovery, experimentation, recommendation systems, regulatory analytics, and high-volume customer data products. I architect batch and streaming pipelines, build LLM and recommendation workflows, and develop internal data services and REST APIs to improve data freshness, experimentation velocity, and production analytics.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

Spotify

May 2021 - Present (5 years 2 months)

Led data engineering initiatives supporting Spotify personalization, discovery, and AI-powered product experiences. Built scalable batch and streaming pipelines in support of recommendation workflows, LLM/recommendation datasets, experimentation, and data/metrics exposure via APIs.

Python SQL Scala BigQuery Dataflow Apache Beam Scio GCP Pub Sub Data Pipelines Experimentation A B Testing LLM Embeddings Recommendation Systems Data Quality fastAPI Flask REST APIs Docker Kubernetes CI CD

Senior Data Engineer

NTT Data

Apr 2018 - May 2021 (3 years 1 month)

Engineered RegTech data services for Bank of England-aligned XBRL analytics initiatives, including Python/FastAPI APIs and MarkLogic NoSQL ingestion workflows. Built XBRL validation/search pipelines and data-quality frameworks (dbt-style tests, Great-Expectations-style rules, Airflow DAGs) to reduce manual review effort and improve dataset reliability.

Data Engineer

Amazon

Aug 2016 - Mar 2018 (1 year 7 months)

Built product analytics pipelines supporting real-time and self-service data products, including clickstream and product interaction pipelines using AWS services. Developed curated data marts and migrated batch reporting workflows into scalable S3-based lake architecture for improved performance and reduced compute costs.