Eugene Mandel
Co-Founder at MuckerLab
Belmont, California, United States
Summary
🤩
Rockstar🎓
Top SchoolEugene Mandel is a founder and AI product leader with nine years of experience building conversational AI, data validation, and customer-insight products that scale in enterprise settings. He has led product and AI efforts at companies from Great Expectations (helping grow the open-source data testing project used by hundreds of teams) to RingCentral and Loris, driving multimillion-dollar revenue from conversation intelligence. Eugene pioneered human-in-the-loop ML for customer support at Directly and applies academic rigor from Conversation Analysis to make conversation analytics actionable for industries like home services. As a hands-on engineer he’s contributed backend and data-engineering work to the prominent Great Expectations codebase, with deep expertise in SQL integrations and data quality workflows. Based in Belmont, CA, he blends startup founding experience (Qualaroo, jaxtr, MustExist) with mentorship at MuckerLab, uniquely bridging academic conversation research and practical AI productization. He’s particularly focused on turning qualitative Conversation Analysis concepts into continuous, automated signals that improve real-world call outcomes.
9 years of coding experience
18 years of employment as a software developer
BS Computer Science, BS Computer Science at Technion - Israel Institute of Technology
Stackoverflow
46reputation
3kreached
2answers
1question
Github Skills (12)
great-expectations10
sqlalchemy10
sql10
python10
data-engineering10
testing9
pandas6
activerecord6
uuid6
dataframe6
ruby-on-rails6
encoding6
Programming languages (2)
Github contributions (5)
great-expectations/great_expectations
Sep 2018 - Nov 2021
Always know what to expect from your data.
Role in this project:
Backend Developer & Data Engineer
Backend Developer & Data EngineerContributions:4 releases, 189 reviews, 863 commits in 3 years 2 months
Contributions summary:Eugene appears to be primarily involved in back-end development and data engineering tasks within the Great Expectations project. Their commits demonstrate expertise in SQL database connectivity and management as well as a focus on data quality and data validation workflows. Furthermore, their code changes and refactoring efforts indicate a strong understanding of the project's data context, store backends, and the intricacies of implementing tests and expectation suites. The user's contributions have focused on improving and extending database integrations, including support for more SQL database and batching options.
pythondatadata-integritydatacleanerpipeline-testing
greatexpectationslabs/ge_tutorials
Feb 2020 - Apr 2021
Learn how to add data validation and documentation to a data pipeline built with dbt and Airflow.
Contributions:2 reviews, 21 commits, 4 PRs in 1 year 2 months
data-qualitypipelinevalidationdbt-packagessql
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