Jeremy Yeo
Staff Solutions Engineer at dbt Labs
Wellington, Wellington, New Zealand
Summary
🤩
Rockstar🎓
Top SchoolJeremy Yeo is a Staff Solutions Engineer at dbt Labs with a decade of experience building data platforms and helping customers adopt engineering best practices for analytics. He combines strong communication and customer-facing skills with hands-on data engineering—contributing code to dbt-core to improve YAML parsing, add context functions, and bolster test coverage. Previously he led customer support at dbt, built ETL and orchestration systems with dbt, Snowflake and Prefect at Sharesies, and created reproducible R analytics tooling and an A/B testing platform at Xero. Jeremy’s background in economics and applied finance informs his pragmatic approach to measurable business outcomes, and he often blends R, Python and Terraform to deliver automated, auditable data products.
11 years of coding experience
10 years of employment as a software developer
Sciences, Sciences at ACS Methodist High SchoolBachelor of Applied Finance Applied Finance and Marketing, Bachelor of Applied Finance Applied Finance and Marketing at University of South AustraliaMaster of Commerce Honours Economics, Master of Commerce Honours Economics at University of AucklandFoundation Business 2006, Foundation Business 2006 at Taylor's University College (TUC)
English, Malay, Chinese
Stackoverflow
493reputation
24kreached
4answers
4questions
Github Skills (18)
python10
viewpoint10
data-modeling10
dbt10
yaml9
pytest8
analytics8
sql7
data-pipeline7
data-pipelines7
knitr6
business-intelligence6
plotly6
shiny6
google-bigquery6
Programming languages (10)
Github contributions (5)
dbt-labs/dbt-core
Mar 2020 - Oct 2022
dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
Role in this project:
Data Engineer
Data EngineerContributions:6 reviews, 13 commits, 7 PRs in 2 years 7 months
Contributions summary:Jeremy contributed to the dbt-core repository by addressing several key aspects of data transformation and validation. They implemented a change to allow an empty string return, added tests to ensure proper functionality, and resolved an issue related to duplicate YAML keys, which improves the robustness of the configuration parsing. Furthermore, the user added set and zip functions to contexts, adding some useful features to the project. These changes collectively enhance dbt's ability to handle and transform data efficiently.
dbtdbt-viewpointslackpypadata-modeling
jeremyyeo/dbt-sandcastles
Sep 2021 - May 2024
Contributions:2 releases, 111 PRs, 251 pushes in 2 years 8 months
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