Taylor Wang
@taylorwang
Staff Data Engineer specializing in scalable, governed real-time data platforms for analytics and AI workloads.
United StatesWhat I'm looking for
I’m a Staff Data Engineer with 10+ years building and modernizing large-scale data platforms across analytics, advertising, payments, personalization, and AI-driven ecosystems. I focus on designing scalable, reliable, analytics-ready data products that improve operational efficiency and support enterprise-wide decision-making.
Most recently at Chewy, I architected an AI-Ready Enterprise Data Platform spanning Snowflake, dbt Cloud, Kafka, Databricks, and AWS—delivering governed analytics and near real-time pipelines with sub-5-minute freshness SLAs. I lead event-driven ingestion and transformation, define standards for data modeling and observability, and implement secure audit and compliance workflows using Unity Catalog, IAM, Delta Lake, and Snowflake governance controls.
Experience
Work history, roles, and key accomplishments
Architected an AI-ready enterprise data platform across Snowflake, dbt Cloud, Kafka, Databricks, and AWS, delivering governed analytics and near real-time data products. Led event-driven ingestion and transformation pipelines with sub-5-minute freshness SLAs and implemented secure audit/compliance workflows using Unity Catalog, IAM, and governance controls.
Engineered the Chewy Customer 360 & Ads Attribution platform using Kafka, Snowflake, dbt, PySpark, and Databricks to support attribution, personalization, retention, and analytics. Improved reporting latency by 15% via multi-touch customer journey pipelines and reduced campaign over-/under-spend by 12% using Airflow + dbt; also built real-time Structured Streaming pipelines.
Built the GoDaddy governed AWS Data Mesh platform enabling decentralized data products across S3, EMR, Glue Data Catalog, Lake Formation, Airflow, and AWS RAM sharing. Supported migration from an 800-node, 2.5 PB Hadoop estate to AWS EMR and improved orchestration reliability and platform observability using Airflow-based modernization and automated monitoring patterns.
Developed the GoDaddy Hadoop-to-AWS ETL Migration Platform modernizing legacy Hive/Oozie workflows into Airflow-managed cloud-native pipelines. Built and validated scalable batch ETL pipelines for domain analytics using Hive, Spark, Python, SQL, AWS S3, and Airflow, improving maintainability through reusable DAG patterns and automated validation checks.
Supported the GoDaddy Domain Search & Conversion Analytics platform built on Hadoop, Hive, Pig, Redshift, Tableau, and legacy MSBI systems. Analyzed customer/search/registration/renewal data using SQL, Hive, and Python, built curated reporting datasets and dashboards, and collaborated to define KPIs and improve metric consistency.
Education
Degrees, certifications, and relevant coursework
Arizona State University
Bachelor's Degree, Computer Engineering
2010 - 2014
Earned a Bachelor's degree in Computer Engineering from Arizona State University (2010–2014).
Tech stack
Software and tools used professionally
Availability
Location
United StatesAuthorized to work in
Job categories
Skills
Interested in hiring Taylor?
You can contact Taylor and 90k+ other talented remote workers on Himalayas.
Message TaylorGet matched with your dream remote job
Sign up now and join over 250,000+ remote workers who receive personalized job alerts, curated job matches, and more for free!
