Meghan Heintz
Founding Engineer at Herd Labs
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolMeghan Heintz is a founding engineer and AI-focused product leader with 11 years of experience building data-first systems and ML tooling from early startups to high-impact product teams. She has led AI initiatives at Dune—shipping conversational analytics, SQL generation and debugging tools, and transforming the Spellbook project into a continuously deployed open-source dbt pipeline that scaled external contributions fivefold. Her background blends hands-on data engineering (notably contributing SQL UDFs and data modeling for duneanalytics/spellbook) with product strategy, infrastructure, and people leadership. Meghan pairs rigorous production practices—CI/CD, data quality testing, and RAG/vector search—with practical ML deployment experience across companies like Rent the Runway and Emerson Collective. Based in New York, she’s comfortable moving between low-level database optimizations and high-level ML roadmap decisions, and she often drives trust-building features such as code-quality ranking and insight vetting that improve user confidence in AI systems.
10 years of coding experience
11 years of employment as a software developer
BS Environmental Resources Engineering, BS Environmental Resources Engineering at Cal Poly Humboldt
Spanish
Github Skills (9)
data-modeling10
postgresql10
sql10
dbt10
data-engineering10
database-design9
data-transformation9
etl8
delta-lake8
Programming languages (7)
Github contributions (5)
duneanalytics/spellbook
Oct 2021 - Jan 2023
SQL views for Dune
Role in this project:
Data Engineer & Database Engineer
Data Engineer & Database EngineerContributions:11 releases, 239 reviews, 152 commits in 1 year 3 months
Contributions summary:Meghan primarily contributed to the `duneanalytics/spellbook` repository by implementing and modifying SQL views for data transformation and analysis, particularly related to blockchain data. Their work focused on creating new UDFs (User Defined Functions) for decoding byte data and integrating the Spellbook dbt project. The user also addressed database-related tasks by removing duplicates from existing tables, enhancing the efficiency of the data aggregation processes, and also by altering table locations to other locations. These changes indicate a focus on refining and optimizing data extraction and storage within the Dune data ecosystem.
sqldatabasemysqldune
duneanalytics/dune-v1-abstractions
Dec 2022 - Dec 2022
Contributions:5 commits, 2 PRs, 3 pushes in 7 days
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