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Patrick Peng

@patrickpeng

Big Data Engineer at Zoom who unified 50+ TB of monthly meeting telemetry in Snowflake, cutting query latency by about 50%.

United States

Message

At Zoom, I engineered a Snowflake analytics platform for more than 50 TB of meeting data each month, enabling cross-team dashboards and reducing query latency by about 50%.

I built real-time telemetry and CDC pipelines with Kafka and Apache Beam, bringing data freshness from hours to under five minutes. I also developed Snowflake models with dbt that supported three times more concurrent analytics queries.

At 84.51°, I orchestrated a Kafka and Spark retail pipeline handling about 500 million customer events a day, reducing data latency by 70%. I also developed Azure Databricks workflows for multi-terabyte grocery transactions.

At SYNCHRONY FINANCIAL, I produced Spark and Python ETL pipelines for credit card transaction data and fraud analytics. Earlier, at NCI TECHNOLOGY, INC., I contributed to product testing and engineering support, and helped release two iOS applications.

Experience

Work history, roles, and key accomplishments

Current

Big Data Engineer

Current

Zoom

Sep 2021 - Present (5 years 1 month)

Engineered a unified Snowflake-based analytics platform ingesting 50+ TB of Zoom meeting data monthly, reducing query latency by ~50%. Built real-time telemetry and CDC pipelines with Kafka and Apache Beam, reducing data freshness to under 5 minutes.

Python DBT Airflow PySpark Databricks Kafka CDC Streams and Tasks Great Expectations Amazon EKS Apache Beam SQL

Senior Data Engineer

84.51°

May 2019 - Sep 2021 (2 years 4 months)

Orchestrated a real-time retail data pipeline with Kafka and Spark, ingesting 500M customer events/day and reducing data latency by 70%. Developed Azure Databricks ELT workflows using PySpark and Matillion, processing multi-terabyte grocery transactions nightly.

Python Snowflake DBT Airflow Matillion PySpark Databricks Kafka CDC Azure Delta Lake SQL

Big Data Engineer

Synchrony Financial

Feb 2018 - May 2019 (1 year 3 months)

Produced large-scale Spark and Python ETL pipelines processing ~500M credit card transactions monthly into AWS Redshift for low-latency fraud analytics. Accelerated Hive and SQL workloads through partitioning and Parquet storage, improving nightly batch performance by ~40%.

Python PySpark Spark Airflow AWS RedShift Hadoop Hive S3 SQL

Business Analyst

NCI Technology, Inc.

Jan 2014 - Jun 2016 (2 years 5 months)

Coordinated R&D project updates, Mandarin-English interpretation, prototype demonstrations, customer support, and product testing, contributing to pass-yield improvement from 85% to 98%. Released two iOS applications to the Apple App Store and supported multiple version updates.