Joey Frazee
Sr. Staff Learning Architect at Databricks
Beachwood, Ohio, United States
Summary
🤩
Rockstar🎓
Top SchoolJoey Frazee is a senior software leader and Sr. Staff Learning Architect with 18 years of experience designing and operationalizing data and ML workflows across cloud platforms. He has held senior engineering and enablement roles at Databricks and Microsoft, blending hands-on engineering, developer enablement, and leadership to help teams ship scalable data-intensive systems. An active open-source contributor, Joey has improved core data-processing projects like Apache NiFi (HL7/XML performance and MIME metadata) and advanced NLP tooling in NLTK with HMM tagger and corpus reader enhancements. His background in linguistics and statistics uniquely informs his approach to ML-driven engineering problems, enabling practical solutions that respect both algorithmic nuance and production constraints. Based in Beachwood, Ohio, he’s known for translating complex distributed-data challenges into teachable, repeatable practices for engineering teams.
17 years of coding experience
16 years of employment as a software developer
Case Western Reserve UniversityLinguistics, Linguistics at University of Texas at AustinBachelor of Arts (BA) Linguistics, Bachelor of Arts (BA) Linguistics at The Ohio State University
Stackoverflow
25reputation
842reached
0answers
3questions
Github Skills (19)
apache-nifi10
python10
nltk10
machine-learning10
java10
ml10
javas10
natural-language-processing10
data-processing10
nlp10
hl710
algorithm9
algorithms9
data-structures8
data-structure8
Programming languages (8)
Github contributions (5)
apache/nifi
Mar 2016 - Sep 2021
Apache NiFi
Role in this project:
Back-end Developer
Back-end DeveloperContributions:165 reviews, 37 commits, 85 PRs in 5 years 7 months
Contributions summary:Joey primarily contributed to improving the Apache NiFi data flow system. Their work involved adding metadata to identify MIME types and resolving issues to ensure proper attribute extraction and segment handling in HL7 message processing, addressing specific bugs and enhancing functionality. They also added caching functionality to improve the performance of XML transformations. Their contributions involved modifications across multiple bundles, with a focus on data processing and transformation logic.
javanifiapacheapache-nifi
nltk/nltk
May 2008 - Apr 2009
NLTK Source
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:72 commits, 1 comment in 10 months
Contributions summary:Joey made significant contributions to the NLTK library, focusing on the ConllChunkCorpusReader, including the addition of methods to read and process data from the CoNLL-2000 corpus. They also enhanced the HiddenMarkovModelTagger by adding functionality to handle unknown words and implementing HMM-based taggers and chunkers. Furthermore, the user introduced new utility classes and refactored existing ones to improve memory usage and performance, demonstrating a focus on both NLP algorithms and code optimization.
nlppythonmachine-learningnltknatural-language-processing
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