Clair Blacketer
Director at Johnson & Johnson
Virginia Beach, Virginia, United States
Summary
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Rockstar🎓
Top SchoolClair Blacketer is a Director at Johnson & Johnson with 11 years of experience applying epidemiology, analytics, and database engineering to pharmaceutical and healthcare problems. She blends hands-on skills in R and SQL-based data warehousing with leadership across epidemiology analytics teams, progressing from analyst to director within J&J. Clair contributes to the widely used OMOP Common Data Model—aligning DDLs across platforms and implementing UTC timestamping and new CDM tables—highlighting her practical impact on interoperable clinical data standards. Currently studying Medical Informatics at Erasmus MC with a focus on data quality, she brings rigorous academic training to operationalize reliable real-world evidence. Based in Virginia Beach, she pairs domain expertise in public health with developer-level database work, enabling both strategic decisions and technical delivery.
10 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at Erasmus MCMaster of Public Health (MPH), Epidemiology, Master of Public Health (MPH), Epidemiology at Eastern Virginia Medical SchoolBachelor of Science, Biology, Bachelor of Science, Biology at James Madison University
Github Skills (12)
sql10
database-design10
impala10
data-warehouse10
datamart10
parquet9
sql-server9
oracle8
amazon-redshift8
aws-redshift8
postgresql8
oracles8
Programming languages (10)
Github contributions (5)
OHDSI/CommonDataModel
Jun 2017 - Sep 2022
Role in this project:
Back-end Developer & Database Engineer
Back-end Developer & Database EngineerContributions:18 releases, 27 reviews, 308 commits in 5 years 4 months
Contributions summary:Clair's primary contribution was converting date columns to use the TO_UTC_TIMESTAMP function for data warehousing in Impala/Parquet and SQL Server. The user updated the SQL scripts to reflect the OMOP CDM version 5.2 and 5.3.1, including adding constraints and new tables such as VISIT_DETAIL and NOTE_NLP. The user was involved in aligning the DDLs across different database platforms (Oracle, PostgreSQL, Redshift, etc.) for improved consistency and functionality.
ddlscdmomopdefinitiondata-model
OHDSI/DataQualityDashboard
Jul 2019 - Oct 2022
A tool to help improve data quality standards in observational data science.
Contributions:7 releases, 40 reviews, 123 commits in 3 years 3 months
pythondata-integrityreconciliationquality-assurancedatacleaning
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