The Problem

Implementing an identity platform across a K-12 district involves three distinct phases. First, the district's data across Active Directory, Infinite Campus, and Munis needs auditing for errors that block provisioning. Second, those errors need tracking through remediation across weeks of back-and-forth with district IT — often across multiple districts simultaneously. Third, the configuration must be validated before go-live. Without dedicated tools, all three phases rely on manual spreadsheet work and email threads.

The Solution

Three connected tools, each handling a distinct phase and feeding the next.

What It Demonstrates

• End-to-end systems thinking — three tools designed as a connected workflow
• Full-stack breadth: Python data processing, vanilla JS browser app with localStorage persistence, Claude API-powered validation tool
• Deep K-12 identity domain expertise: provisioning workflows, source system reconciliation, OU configuration, dynamic group management
• Designing for real users under real constraints — used live on client calls, built to handle messy real-world data

Screenshots

Tool 1 — Audit Generator · Excel output with color-coded remediation dashboards

Tool 2 — District Tracker · multi-district remediation tracking with DVR delta history

Tool 3 — Mapping Validator · Claude API-powered scored configuration review

Overview

A Python-based desktop utility that transforms structured data files based on configurable rule sets. Users define conditional transformation rules and apply them to operational datasets through a graphical interface — no command line required.

Use Case

Transforming structured operational files where specific column values must be updated based on defined conditions before ingestion into another system. Built to eliminate repetitive manual file modification and reduce pre-ingestion data preparation errors that accumulated across the operational cycle at GMRS.

What It Demonstrates

• Translating a repetitive manual workflow into a reusable, configurable tool
• Python GUI development with Tkinter for non-technical end users
• Data transformation logic using Pandas applied to real operational file structures

Screenshot

File Transformer GUI — configurable rule blocks, source/destination column mapping

Overview

Built to merge datasets from multiple sources into a unified structure suitable for reporting and analysis — eliminating manual consolidation work that was error-prone and time-consuming across GMRS's multi-project environment.

The Problem It Solved

Operational data at GMRS originated from multiple separate systems and file formats. Combining them for reporting required manual work that introduced inconsistencies and consumed significant staff time every cycle. This tool codified the merge logic and made it repeatable and auditable.

What It Demonstrates

• Multi-source data pipeline design — normalizing and merging sources correctly, not just reading files
• Turning a manual, error-prone process into a reliable, repeatable tool
• Python/Pandas data engineering applied to real operational workflows

Screenshot

Spreadsheet Combiner — multi-file selection, header mapping, 1,847 rows combined across 5 sources

Overview

Merges and standardizes disposition-level data from multiple sources into a unified reporting dataset. Replaced manual consolidation processes, reducing errors and significantly improving data consistency for operational reporting and analysis across GMRS research campaign lifecycles.

What It Demonstrates

• Applied automation to a high-frequency, error-prone operational task
• Data standardization logic across inconsistent source formats
• Python/openpyxl for structured Excel output generation

Screenshot

DispoMerge — GUI with log output showing 4,812 records processed, 421 duplicates removed, 3,200 merged and saved

Overview

A Streamlit web application enabling non-technical administrators to configure, audit, and export identity platform dynamic group mapping rules across Active Directory and Google Workspace — without touching raw configuration files or requiring technical knowledge of the underlying systems.

What It Replaced

Manual reconciliation of group mapping rules that required technical staff to directly inspect and compare configurations across systems. The engine made this accessible to implementation managers and reduced reconciliation from hours to minutes.

What It Demonstrates

• Streamlit application design for non-technical end users in a production environment
• Deep knowledge of identity platform group rule architecture
• Building tools that increase operational capacity without adding technical headcount

Screenshot

District Logic Engine v5.1 — Seattle_Unified, 847 groups loaded · 612 synced, 143 AD Only, 67 Google Only, 25 Missing

The Problem

Remote job listings are scattered across dozens of platforms with inconsistent formatting, duplicate postings, and no unified view. Manually tracking and comparing listings across sources is time-consuming and error-prone — exactly the kind of problem that should be automated.

What It Does

• Pulls live data from RemoteOK API, We Work Remotely RSS, and Remotive API simultaneously
• Merges all records into a unified stream and deduplicates across sources
• Normalizes data structure and field values across sources with inconsistent schemas
• Routes records into Master Table and Daily Feed views in Airtable via upsert
• Generates audit, normalization, dedupe, and combo reports for pipeline visibility

What It Demonstrates

• Practical workflow automation using n8n node-based architecture
• Multi-source data ingestion, transformation, and normalization across inconsistent APIs
• Real-world application — built to solve an actual operational problem, not as a demonstration

Screenshots

n8n workflow canvas — three parallel API ingestion paths merging into dedupe, normalization, and Airtable upsert

Airtable output — normalized job listings from RemoteOK, We Work Remotely, and Remotive

Overview

An Excel-based forecasting model projecting staffing requirements and workload demand across multiple shifts based on historical trends. Built for a 300+ employee, multi-project call center operation where accurate staffing projection directly affected production capacity and client deliverable timelines.

Part of a Broader Financial Framework

One component of a three-part financial operations suite — alongside the CPI Calculator and Multi-Study Profitability Tracker — that together served as the central financial intelligence layer for GMRS executive decision-making.

What It Demonstrates

• Applied Excel modeling for operational forecasting in a complex, dynamic environment
• Understanding of how staffing projections connect to production outcomes and financial results
• Building systems that translate raw operational data into decision-ready outputs

Screenshot

Forecasting Model — Notes tab, manual entry with 5-shift projection results, and auto-calculation engine

Overview

A scalable reporting and analytics framework consolidating multi-source operational, staffing, and performance data into a single source of truth. Designed to track 50+ metrics per shift across campaign lifecycles with auto-calculated KPIs and a structured data entry architecture — replacing informal, inconsistent reporting practices across all active research campaigns at GMRS.

What It Replaced

Informal, ad-hoc shift reporting that varied by supervisor and couldn't be reliably aggregated or audited. The framework standardized what was captured, how it was captured, and how it connected to downstream financial and billing systems.

What It Demonstrates

• Systems thinking at the operational level — connecting shift-level data to executive-level visibility
• Excel architecture for structured data entry that maintains integrity at scale
• Reporting design that serves multiple stakeholders: floor supervisors, operations management, and executive leadership

Screenshot

Raw supervisor shift input (left) → auto-calculated KPI summary report (right) · Study X0014, Banking Customer Communications

Overview

A financial operations dashboard built using simulated transaction data to replicate real-world reporting scenarios. Developed KPI frameworks for authorization rates, failure rates, and processing performance, with visualizations for trend analysis and operational monitoring.

Design Intent

Built to demonstrate clean data structure, reporting accuracy, and actionable insights at a production level — not a toy example, but a genuine template for the kind of financial reporting used to monitor transaction processing operations. Emphasized structural clarity and reproducibility.

What It Demonstrates

• Financial KPI framework design: authorization rates, failure rates, processing performance
• Dashboard layout and visualization design for operational decision-making
• Clean data architecture that supports reliable, reproducible reporting

Screenshots

Source data — raw transaction CSV with TransactionID, Status, Amount, PaymentMethod, CardType, AuthTimeMs

Structured table — 60% auth rate, 20% failure rate, 213ms avg processing time

Pivot analysis — processing time by status, volume by date, channel, and outcome

Dashboard — transaction by status, avg processing time, payment method breakdown, daily volume with narrative commentary

Overview

A two-component financial toolkit for research study management: a pre-field cost estimator projecting the cost-per-interview before a study begins, and a post-field profitability tracker comparing true operational cost against client billing across all concurrent studies.

Why It Mattered

In a multi-client call center running dozens of concurrent research studies, billing accuracy and profitability visibility were critical. Without systematic tracking, individual study margins were invisible until invoicing — by which point it was too late to course-correct. This toolkit brought real-time financial visibility to every active study.

What It Demonstrates

• Financial modeling applied to operational research environments
• Pre-field / post-field analytical framework design
• Multi-study ledger architecture that scales across concurrent client engagements
• Understanding of how operational decisions connect directly to financial outcomes

Screenshots

CPI Calculator — Study X0001, CNO Financial · $45 bid vs $15.17 actual CPI, incidence rate +20% above estimate

Profit Evaluator — 12 studies, $27,895 cost, $158,420 billed, +$130,133 net · 11 profitable, 1 at loss

Overview

Salesforce reporting solutions designed and maintained to monitor support operations, track key performance metrics, and analyze customer trends at Identity Automation — built for cross-functional stakeholders with focus on data integrity, report accuracy, and usability.

Key Applications

• Built reporting to track adoption trends across the K-12 customer portfolio
• Monitored support activity and operational risk indicators by customer and district
• Designed dashboards supporting cross-functional visibility for CSE, TAM, and leadership teams
• Maintained data integrity across CRM records tied to active implementations

Project Walkthrough — "Designing Dashboard Mockups as a Salesforce Professional"

A Clicked project demonstrating end-to-end Salesforce dashboard design: requirements gathering from a stakeholder interview, custom object + field creation, mock data import via the Data Import Wizard, report building, and final dashboard delivery on desktop and mobile.

Project overview — Designing Dashboard Mockups as a Salesforce Professional

Step 5 — Report creation · MDF custom objects available for report type selection

Step 7 — Dreamforce Registration Dashboard · desktop and mobile views · Real-Time Activities, Attendee Behavior, Two-Way Comms, Automated Tasks, Compliance