Your data platform is slow, expensive, and distrusted. I fix that.

Business impact

Technical achievements

• Replaced hours-long manual Airtable workflows with a Python-based ontology management and reporting system that standardized metadata, preserved historical accuracy, and ensured consistent data availability for ML model generation.
• Accelerated new report release cycles by 90% by building a Tableau deployment automation tool that used the Tableau API to clone and republish dashboards across BigQuery datasets with repeatable configuration.
• Created a self-serve reporting framework that removed BI bottlenecks and drove a 200% increase in model generation throughput across the company.
• Improved model build performance by 3x by scripting data processing and pipeline orchestration in Nextflow, making builds more predictable, resumable, and easier to scale.
• Clarified the company's overarching data model by combining hive partitioning with granular data improving data discoverability and query performance.
• Improved data trust by enforcing strict dbt modeling discipline, source testing, and relationship validation, which eliminated critical-level support tickets tied to broken or inconsistent data contracts.

Responsibilities

• Administered BigQuery datasets, access patterns, partitioning strategy, and reporting-layer reliability for the ML platform.
• Served as the Tableau administrator, managing dashboard deployments, access controls, and automation workflows.
• Maintained the accuracy of the model's metadata via update scripts and automated validation checks.

Business impact

Technical achievements

• Re-engineered backend SQL in BigQuery with more efficient CTE patterns, materialized views, and optimized data models to reduce reporting load times by more than 75% for internal stakeholders.
• Designed a real-time operational dashboard suite that surfaced high-cost queries and heavy usage trends, helping reduce Google Cloud spend by 10% company-wide.
• Built a Jira-to-BigQuery ETL pipeline in TypeScript to ingest project and resource data for executive dashboards, improving delivery speed by 15% and giving leadership better portfolio visibility.
• Served as the central owner for Tableau dashboard architecture, optimization, and stakeholder communication across departments, enabling better decision-making at the executive level.

Responsibilities

• Owned the BI platform and translated executive and cross-functional reporting needs into maintainable data products.

Business impact

Technical achievements

• Increased record-matching speeds by 50% and laid the groundwork for a predictive pricing AI model by implementing an NLP-based entity resolution workflow for insurance plan matching.
• Converted brittle CSV-based workflows into SQLite-backed data stores, enabling faster querying, anomaly detection, and operational diagnostics for reimbursement trend analysis while preventing file corruption issues.
• Cut five hours of weekly manual list generation by building an automated routing pipeline in Python that sent unmatched data exceptions directly to the clinical call team.
• Used data quality monitoring and exception handling to improve the reliability of pricing and reimbursement datasets used by upstream operational teams.

Business impact

Technical achievements

• Built an automated performance tracking system to monitor provider adherence on key operational metrics such as patient leakage and preventive care, improving goal adherence by more than 20%.
• Used targeted SQL models to identify high-risk, high-cost patient cohorts for post-hospitalization follow-up, helping reduce 30-day readmission rates by one-fifth.
• Translated operational needs into reliable, repeatable reporting and analysis that supported risk and performance management decisions across the business.

Responsibilities

• Owned recurring risk and performance reporting for provider adherence, patient leakage, preventive care, and follow-up operations.
• Translated business questions into SQL analyses and cohort definitions for clinical and operational stakeholders.
• Maintained reliable reporting workflows used to monitor goals and prioritize interventions across high-cost patient populations.

Business impact

Technical achievements

• Established normative CI/CD pipelines to streamline development and deployment processes.
• Edited the Lexicon codebase to improve readability and maintainability as well as compliance with CakePHP conventions.
• Integrated automated testing to ensure code quality and reliability.
• Optimized the performance of the Lexicon codebase to handle large datasets efficiently.

Responsibilities

• Maintain the network of Jewish Language lexicons across 10+ languages.
• Implement enhancements to the Lexicon codebase to improve functionality and usability.
• Pull data from various Jewish language lexicons for research and analysis purposes.