Data & Analytics Engineer who builds the pipelines, warehouses, and models that turn messy, multi-system data into numbers people trust. I've designed modern data stacks (Dagster, dbt, dlt, BigQuery) end to end, from ingestion and data quality checks to the semantic layers, dashboards, and production ML models stakeholders rely on. I bring a software engineering mindset—CI/CD, testing, and modular design—so data systems stay maintainable, observable, and useful.
Experience
Data Engineer / Customer Delivery Engineer (Contract)
Innovare · Chicago, Illinois (Remote)
Mar 2026 — Present
Pipeline Reliability: Own the stability and scalability of production data pipelines feeding high-stakes EdTech dashboards, ensuring datasets remain accurate and auditable for enterprise school district customers.
Medallion Architecture Design: Co-designing a transition to a Medallion (bronze/silver/gold) data architecture on BigQuery, defining layer contracts, freshness SLAs, and lineage to improve traceability and downstream analytics velocity.
Modern Stack Advocacy: Proposed adopting Terraform (IaC), dlt (ingestion), dbt (transformation), Dagster (orchestration), Cube.js (semantic layer) to replace ad-hoc workflows — enabling version control, automated testing, and unified observability across the platform.
Workflow Automation: Replacing manual customer delivery workflows with automated, idempotent pipelines, reducing turnaround time and eliminating recurring sources of data drift between systems.
Cross-System Integration: Manage complex data flows across disparate EdTech sources, designing for schema evolution, late-arriving data, and the operational realities of multi-tenant customer delivery.
Data Scientist (Lead: Data Engineering, MLOps) - Ed Pioneers Fellow
Caliber Public Schools · Richmond, California
Sep 2024 — Oct 2025
Modern Data Stack Architecture: Architected and built a scalable platform on GCP. Orchestrated ELT pipelines using Dagster, dbt, and dlt to ingest data from disparate SIS (School Information Systems) and HR platforms into BigQuery.
Engineering Maturity (ROI): Engineered a comprehensive People Team data pipeline, reducing manual consistency checks from months of collective annual work to seconds. Built a custom automated data quality framework to catch inconsistencies between systems.
API Integration & Interoperability: Solved complex data interoperability challenges by designing integrations between Schoolmint and PowerSchool. Reverse-engineered undocumented APIs to create a unified data model for cross-functional analysis.
Solo Data Lead: Owned the full data lifecycle as the sole data professional, partnering directly with C-suite and department heads to move the org from manual spreadsheets to automated warehousing.
Predictive Modeling: Built and deployed an explainable staff-turnover model (Logistic Regression, Deep Learning) with a tunable risk threshold so leadership could match precision/recall to hiring capacity.
Data Scientist (Data Engineering, MLOps)
SetSail · San Mateo, California
Aug 2021 — Feb 2023
Pipeline Architecture (AWS): Led a critical overhaul of the AWS data infrastructure (S3, Athena, EMR). Implemented 'SQL Push-down' strategies and asynchronous DAGs, reducing data processing latency by 75% and scaling to multi-terabyte datasets.
Scalable Data Modeling: Architected scalable Star Schema data models and optimized ETL/ELT processes, ensuring data readiness for ML and LLM integration.
Engineering Best Practices: Championed the adoption of CI/CD pipelines (GitHub Actions), unit testing (pytest), and Agile methodologies within the data science team.
Business Impact: Contributed to infrastructure enhancements that achieved 33% faster ramp times, 16% higher revenue, and 15x ROI for customers.
Production ML: Developed and deployed propensity scoring and churn models, using NLP on unstructured email metadata to surface sales signals.
Education
University of California, Berkeley
Bachelor of Arts in Data Science (Domain Emphasis: Quantitative Social Science)
GPA: 4.00/4.00
Highest Distinction (Summa cum laude). Outstanding Data Science Undergraduate Award (Top of Class).
Executive Presentation: Presented data-driven insights to chorus leadership.
Mentor and Trans Support Leader
San Francisco Gay Men's Chorus
Jan 2018
— Present
Support members through mentorship and leadership within trans support initiatives, coordinating meetings and events.
Peer Mentorship: Provided mentorship and peer support to chorus members.
Community Leadership: Led coordination for trans member support group meetings and events.
Inclusive Culture: Contributed to fostering an inclusive environment within the organization.
Summary
Data Scientist expertly blending rigorous social science research with production-grade engineering. I specialize in the full ML lifecycle—translating complex data into actionable insights and deploying explainable predictive models that drive measurable business outcomes. I am focused on bridging the gap between statistical foundations and scalable, ethical applications.
Experience
Data Scientist / Customer Delivery Engineer (Contract)
Innovare · Chicago, Illinois (Remote)
Mar 2026 — Present
Analytics Layer Stewardship: Own the correctness and interpretability of the EdTech analytics layer, ensuring metrics, segmentation, and dashboards remain trustworthy for school leadership decision-making.
Stakeholder-Ready Insights: Deliver presentation-ready analyses for enterprise accounts, translating raw operational data into segmented, decision-driving narratives for non-technical school leaders.
Customer-Facing Analysis: Diagnose data quality and metric discrepancies for enterprise customers, applying statistical reasoning and domain knowledge to surface root causes rather than just symptoms.
Analytics Architecture Input: Consulted using data engineering experience on the company's data architecture roadmap, planning for a Medallion structure on BigQuery to give analytics workflows reproducible, well-tested foundations.
Workflow Automation: Automating recurring analytics workflows so the team can spend more time on high-leverage exploratory analysis and less on manual report production.
Data Scientist (Lead: ML, Data Engineering, MLOps) - Ed Pioneers Fellow
Caliber Public Schools · Richmond, California
Sep 2024 — Oct 2025
Predictive ML & Risk Modeling: Developed and deployed explainable ML models (Logistic Regression, Deep Learning) to predict staff turnover. Engineered a 'Risk Tolerance' configuration allowing non-technical leadership to adjust precision/recall thresholds based on quarterly hiring capacity.
Strategic Leadership (Solo Data Lead): Owned the full data lifecycle (DS, DE, ML) as the sole data scientist. Partnered directly with C-suite and department heads.
Survey Design & Enrichment: Leveraged data to enrich staff satisfaction surveys with demographic, work location, role, grade level, and tenure data, enabling highly segmented and actionable insights.
Data Democratization: Designed data literacy training modules, empowering school leaders to access real-time attendance and academic metrics without technical bottlenecks.
Stakeholder Management: Partnered with leadership on high-impact, data-driven solutions; presented findings to stakeholders including the Board of Directors.
Data Scientist (ML, NLP, Causal Inference)
SetSail · San Mateo, California
Aug 2021 — Feb 2023
Production ML (Revenue): Developed and deployed production ML models for Propensity Scoring (deal closure probability) and Churn Modeling. Leveraged NLP on unstructured email metadata to identify sales signals.
Business Impact: Contributed to product enhancements that achieved 33% faster ramp times, 16% higher revenue, and 15x ROI for customers.
Causal Analysis: Performed deep causal inference studies to isolate specific sales behaviors that drive outcomes, influencing the product roadmap to focus on 'high-leverage' user actions.
Scalable Data Modeling: Architected scalable Star Schema data models and optimized ETL/ELT processes, ensuring data readiness for LLM integration.
Technical Consulting: Acted as a technical consultant for enterprise customers, diagnosing complex data discrepancies and proposing architectural solutions.
Data Science Research Team Lead
UC Berkeley School of Public Health · Berkeley, California
Sep 2020 — May 2021
Leadership: Led data science components for mixed-methods studies on equity and public health (specifically violence against homeless youth). Managed a team of undergraduates.
Unstructured Data: Analyzed diverse unstructured and non-traditional datasets (qualitative interviews, geospatial data, text corpora, hand-drawn maps) requiring the development of novel data processing approaches.
Geospatial Analysis: Performed geospatial analysis (ArcGIS) to identify and visualize spatial violence patterns for non-technical stakeholders.
Visualization: Created interactive dashboards (Tableau, Plotly) to communicate findings to stakeholders.