Webster Cook is a senior software engineer with over a decade of experience designing and shipping data warehouses and robust ETL pipelines for analytics at scale. He has deep expertise in Airflow, Spark, Redshift and Kubernetes, applied across fintech and crypto-focused companies where he built GDPR-compliant warehouses, clustering pipelines for Bitcoin analytics, and production blockchain data loaders. Webster’s open-source work includes contributions to ethereum-etl-airflow, developing idempotent Redshift load DAGs and schema designs that simplify getting smart contract data into analytics systems. With a background in actuarial science, he pairs statistical rigor and data testing practices with hands-on distributed-systems engineering to turn messy, high-volume data into auditable insights.
10 years of coding experience
10 years of employment as a software developer
B.S. Actuarial Science, B.S. Actuarial Science at University of Central Florida
Airflow DAGs for exporting, loading, and parsing the Ethereum blockchain data. How to get any Ethereum smart contract into BigQuery https://towardsdatascience.com/how-to-get-any-ethereum-smart-contract-into-bigquery-in-8-mins-bab5db1fdeee
Role in this project:
Data Engineer
Contributions:34 commits, 2 PRs, 7 comments in 2 years 10 months
Contributions summary:Webster primarily focused on developing and implementing the data loading process for Ethereum blockchain data into a Redshift data warehouse. Their contributions include designing the Redshift schema, creating a load DAG for Redshift, and configuring the loading process for both CSV and JSON file formats. The user also added support for idempotent data loading using merge operations and handled data type conversions.
Contributions:88 commits, 82 pushes, 2 branches in 1 month
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