For hiring managers · 30-second scan

Find what you came for

Every resume claim, mapped to where it can be verified in this repo or on a live site. Plus impact numbers with methodology so you can read them critically.

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If you have 30 seconds

The headline numbers, with the source for each. Read the methodology section below before quoting any of these.

60%
Reduction in manual reporting effort
Recurring data pulls, transformations, and report generation moved to Python automation. Methodology below.
1,000+
Business users supported
Power BI & SQL reporting across collections, fraud, compliance, servicing.
7+ yrs
Regulated fintech
Sallie Mae Bank · April 2018–present · federally regulated student lending.
6
AI-generated validation categories
The Builder generates Snowflake test cases across completeness, uniqueness, referential integrity, range/value, freshness, reconciliation.
3
Production PWAs on a unified backend
IronLog, FinanceLog, NutritionLog · solo build, Supabase + Vercel + Cloudflare R2.
21
SQL challenges in the playground
Snowflake-shaped lending schema, in-browser SQLite via WebAssembly. Try them: SQL Playground →

Resume claim → where to verify it

One row per resume bullet. Click through to the page or live site that demonstrates the work.

Resume claim Where to verify
Own end-to-end Power BI & SQL reporting across collections, fraud, compliance, servicing — supporting 1,000+ users. Power BI Dashboards · Collections · Fraud · Compliance · Servicing
Write & optimize advanced Snowflake SQL powering executive dashboards, reconciliation, and operational analytics. SQL Playground case study · Live playground (21 challenges, window functions)
Translate ambiguous reporting needs into scalable BI solutions, standardized KPI definitions, documented frameworks. Operations Command Center case study — KPI definitions, information hierarchy, decision log.
Reduced manual reporting effort by 60% via Python automation. The Builder case study — pipeline architecture & release flow. Methodology in the section below.
Built & maintain data pipelines integrating Python, Snowflake, SQL — automated documentation & QA workflows. The Builder · CI/CD discipline · SAS jobs
Designed an AI-augmented testing platform generating Snowflake test cases across six categories. The Builder — six categories explicit: completeness, uniqueness, referential integrity, range/value, freshness, reconciliation.
Ship full-stack engineering work — Python, TypeScript, React — bridging product gaps for internal stakeholders. IronLog evidence · PartnerPulse ↗ · Refi Intelligence
Own Infrastructure-as-Code & CI/CD discipline for analytics tooling. CI/CD page · branch-review & merge-control patterns shown in The Builder
Drive internal adoption — communicate platform capabilities, train users, position BI as self-service. Education suite (cohorts, modules, library) · Operations Command Center rollout narrative
Plan & prioritize the reporting roadmap — communicate progress, risks, tradeoffs to business & technical leadership. Evident in case-study narratives: Operations Command Center, The Builder, SQL Playground.
Maintain compliance with regulatory, data security, audit standards in federally regulated financial services. Compliance matrix · audit-trail design in The Builder

How to read the impact numbers

One paragraph per number — what was measured, how, and what's intentionally not claimed.

60% reduction in manual reporting effort
Methodology
  • What: recurring data pulls, transformations, and report generation that previously consumed analyst hours each cycle.
  • How measured: baseline analyst hours per reporting cycle before automation vs. analyst hours per cycle after — same scope, same outputs.
  • Mechanism: Python automation of recurring SQL pulls and transforms; report generation triggered from the pipeline rather than hand-run.
  • Not claimed: faster query performance, lower compute spend, or improved report accuracy — those are separate effects, not part of this number.
  • Caveats: directional figure based on internal team measurement; not externally audited.
1,000+ business users supported
Methodology
  • What: distinct internal business users with access to Power BI / SQL reporting outputs I own end-to-end.
  • Scope: across collections, fraud, compliance, and servicing domains.
  • Not claimed: daily-active users — total addressable user base, not engagement.
Six AI-generated validation categories
Methodology
  • Categories: completeness, uniqueness, referential integrity, range/value, freshness, reconciliation.
  • How they work: structured inputs describe the dataset; AI generates Snowflake test cases per category; tests run as part of the pipeline.
  • Where to see it: The Builder case study.
Three-app PWA suite, solo
Methodology
  • What: IronLog, FinanceLog, NutritionLog — three separate React/Vite PWAs sharing one Supabase backend.
  • Solo: sole designer + engineer · concept → production · ongoing operator.
  • Verifiable: visit ironlog.space, install the PWA, sign in across the apps with the same account.
  • Architecture proof: IronLog evidence page.

How to verify what's here