Current focus
I work across data engineering, analytics engineering, product measurement, and software-adjacent execution. The through-line is practical trust: take fragmented inputs, define the logic clearly, and turn them into outputs that people can actually use.
Now
Warehouse models, identity stitching, reporting reliability, and revenue visibility.
Before that
Metrics governance, reporting workflows, and clearer decision support for teams under pressure.
Still true
Domain work that keeps the portfolio grounded in public systems, incentives, and real-world trade-offs.
Featured work
The first layer is recruiter-fast: what was built, why it mattered, and what kind of system or decision problem it solved. The deeper layer is there for people who want the architecture, trade-offs, and domain context.
Work-inspired
Unified CRM, product, billing, and support data into an account-level model that made GTM revenue reporting usable and trustworthy.
Shows identity stitching, attribution logic, and cross-functional metric governance in a messy real-world environment.
Work-inspired
Built a layered ELT platform with tests, monitoring, lineage, and reusable patterns to make analytics data dependable.
Signals production-minded data engineering, not just one-off pipeline work.
Work-inspired
Standardized event instrumentation and experiment readouts to connect product changes with activation and onboarding outcomes.
Demonstrates product analytics rigor, not just dashboarding.
Personal
Investigated airfare pricing and fairness risk through predictive modeling and algorithmic bias analysis.
Keeps the portfolio tied to transportation, economics, and practical fairness trade-offs.
Research
Evaluated access, safety, and connectivity around transit hubs through GIS and urban planning analysis.
Preserves the transportation and urban systems through-line that makes the site distinctive.
How I work
I am usually most useful when the process is messy, the definitions are unstable, and the downstream decision matters. That is where better warehouse logic, better KPI discipline, and better product measurement can change whether a team trusts its own system.
The site keeps transportation, planning, and fairness in the mix because those interests are part of the same instinct. They shape how I think about throughput, incentives, public systems, and the edge cases that polished dashboards usually hide.
Next move
Start with Experience for the strongest work-backed proof. Then use Projects for the broader library, or About if you want the longer through-line across systems, transportation, and planning.