o Own product usage data end to end as its primary owner: the nine core pipelines that are our source of truth for product usage, 300+ dbt models, and 700+ tests, used by Customer Success, Sales, Product, and engineering

o Took on ingestion over the past year, landing new session-level product data streams in Snowflake on Airflow, validating them against known platform activity, and helping cut data latency from about an hour to under five minutes

o Built shared logic giving those streams a common plan ID and timestamp structure, including session-level plan attribution, and moved them from test into our enterprise Snowflake environment

o Built a human-in-the-loop Claude agent that checks the source topics in our data lake weekly, flags in Slack where a model is reading an older topic version, and opens a pull request with the fix; its first run caught 40+ models reading outdated source topics

o Led the migration of about 40 A Cloud Guru reports from Looker to Tableau and Snowsight after the acquisition, moving the report logic into dbt models, and led the audit when that product data moved into our enterprise Snowflake environment

o Mentor new engineers and lead code reviews on dbt patterns, SQL optimization, and analytics engineering practice

o Led organization-wide adoption of dbt after training at a dbt conference, setting the testing, documentation, and modular design standards the analytics team followed

o Designed and implemented the analytics layer of the enterprise data warehouse in dbt: 50+ tested and documented models serving all four Universal Service Fund program areas under FCC oversight

o Owned end-to-end predictive analytics pipelines in R, from fraud detection to propensity to sign up for a service, with Great Expectations data quality checks and performance monitoring

o Built dashboards and reporting in Tableau and R Shiny with program stakeholders, designing the underlying models around the decisions they needed to make

o Integrated legacy source systems into tested star schema data marts so stakeholders could self-serve while data governance held

o Mentored three new analysts on dbt, SQL optimization, and dimensional modeling through code review

o Designed and taught graduate-level courses on data science for public policy students, covering SQL, Python, R, data warehousing, version control, machine learning, and text analysis

o Mentored 60+ students through code reviews and office hours on analytics best practices, reproducible workflows, and technical communication

o Guided students in collecting real-world datasets, designing statistical models, and conducting analyses to inform policy proposals

o Oversaw development of machine learning projects evaluating solutions for public sector challenges, including NYC transit system optimization

o Built data pipelines using Python, Airflow, and PostgreSQL to enable ongoing analysis of publication and citation data, evaluating the university’s academic performance relative to peer institutions

o Designed and maintained a multi-decade data warehouse supporting strategic decision-making on PhD retention rates, faculty performance, and program outcomes

o Managed data collection for a multi-state precinct-level election research project, supervising 3 undergraduate researchers and organizing data for use by academic collaborators

o Collaborated with faculty on dataset collection, analysis, and visualization supporting research publications using R, STATA, and ArcGIS

o Designed course materials, planned lessons, provided constructive feedback to students, and led sections in a range of courses, such as American Government and Politics, Introduction to GIS for Social Science Research, and Advanced Statistical Methods for Social Science

o Developed a remote lab process to increase access and efficiency for students and faculty using ArcGIS

o Created dummy and real datasets to demonstrate complex models and mapping processes in R, STATA, and ArcGIS