I'm a data-driven BI professional bridging complex enterprise data and corporate strategy. I specialize in Python, SQL, and Power BI within Microsoft-based ecosystems, delivering end-to-end data analysis—from database querying and ETL to interactive dashboards and stakeholder reporting. My work thrives on turning data into actionable insights that drive measurable value, and I enjoy collaborating with cross-functional teams to raise data literacy and ensure standardized metrics like ISO 9001 are embedded in daily operations.…

Gabriel Flynn

I'm a data-driven BI professional bridging complex enterprise data and corporate strategy. I specialize in Python, SQL, and Power BI within Microsoft-based ecosystems, delivering end-to-end data analysis—from database querying and ETL to interactive dashboards and stakeholder reporting. My work thrives on turning data into actionable insights that drive measurable value, and I enjoy collaborating with cross-functional teams to raise data literacy and ensure standardized metrics like ISO 9001 are embedded in daily operations.…

Available to hire

I’m a data-driven BI professional bridging complex enterprise data and corporate strategy. I specialize in Python, SQL, and Power BI within Microsoft-based ecosystems, delivering end-to-end data analysis—from database querying and ETL to interactive dashboards and stakeholder reporting.

My work thrives on turning data into actionable insights that drive measurable value, and I enjoy collaborating with cross-functional teams to raise data literacy and ensure standardized metrics like ISO 9001 are embedded in daily operations.

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Language

English
Fluent

Work Experience

Operational Excellence Process Professional at Ingram Micro
May 1, 2024 - Present
Extracted, cleaned, and analyzed data feeds from Microsoft SQL Server 2.0 and Visual Studio Code with Python to provide business insights for key stakeholders. Developed user-friendly KPI dashboards using Power BI, Excel, and Python to inform operational performance. Identified $150,000 in annual savings using a linear regression model identifying process flow disruptions leading to extended cycle times. Implemented strategy deployment initiatives to drive 1-, 3-, and 5-year goals. Trained cross-functional teams in statistical analysis to enhance root-cause analysis and project execution. Led cross-functional teams on ESG, operational expense improvements, safety and lean initiatives. Developed and executed facility audit programs to document and maintain ISO-standardized metrics. Deployed a Power BI report across North America tracking hourly quality defects, reducing 16 hours of daily indirect time.
Outbound Operations Manager at Techtronic Industries
December 1, 2023 - April 30, 2024
Developed daily, weekly, and monthly staffing plans for the E-commerce, Picking, and Shipping departments by analyzing sales forecasts. Ensured customer requirements for outbound shipments were met for 40 different customers. Tracked CPU, efficiency, utilization, and service level agreement metrics to monitor and enhance department productivity. Coordinated cross-functional projects to improve process efficiency and eliminate non-value-added time. Configured department layouts to improve ergonomics and proficiency. Executed order management and monitored department flow with Oracle R12.
Process Engineer at Techtronic Industries
April 1, 2022 - December 31, 2023
Designed the current process and physical layout for the picking department, generating annual savings of $127,000. Developed regression models utilizing Python 3.11 to forecast unit sales for item numbers to enhance inventory usage. Performed time studies on all direct tasks to observe and measure processing times, leading to staffing models that reduced the facility CPU from $1.20 to $0.53. Assisted with API POST and PUT calls between multiple operating systems enabling real-time data analysis. Managed short- and long-term projects between cross-functional teams to enhance business process flow. Integrated an automated picking solution utilizing AMRs.

Education

Bachelor of Marketing and Finance at University of Texas at El Paso
April 1, 2016 - May 1, 2021
Bachelor of Marketing and Finance at University of Texas at El Paso
April 1, 2016 - May 1, 2021

Qualifications

PYTHON BASICS FOR DATA SCIENCE
January 11, 2030 - April 27, 2026
Analyzing Data with Python
January 11, 2030 - April 27, 2026
SQL for Data Science with R
January 11, 2030 - April 27, 2026
SPY0101EN: Python Basics for Data Science
January 11, 2030 - April 28, 2026
DA0101EN: Analyzing Data with Python
January 11, 2030 - April 28, 2026
RP0203EN: SQL for Data Science with R
January 11, 2030 - April 28, 2026

Industry Experience

Manufacturing, Transportation & Logistics, Wholesale & Distribution, Professional Services, Software & Internet, Retail
    paper Stock Market Quant Analytics Trading Tool

    SOAR: Scaling Operations & Analytical Resources

    A High-Performance Data Pipeline & Interactive Intelligence Engine

    [VIEW APPLICATION](https://www.twine.net/signin

    🚀 The Mission: Operational Visibility at Speed

    In modern operations, the biggest “waste” isn’t on the shop floor—it’s in the data. Decisions are often delayed by manual spreadsheet updates, disparate APIs, and stale reporting.

    Project Overview
    SOAR is a full-stack technical proof-of-concept designed to solve the “Data Velocity Problem”—the challenge of transforming volatile, disparate data into actionable signals. While this instance tracks macroeconomic indicators and asset performance, the underlying architecture is a blueprint for any business requiring real-time visibility into complex, high-stakes systems.

    The Business Problem
    In many organizations, the biggest “waste” is the manual processing of data. Decisions are often delayed by:

    Manual spreadsheet updates (Labor Waste).

    Data trapped in siloed APIs or software.

    Lack of real-time visibility into key performance indicators (KPIs).

    The Solution: A “Lean Data” Architecture
    I developed SOAR to demonstrate how Lean Management Principles and Modern Data Engineering can eliminate these inefficiencies. The project proves that any business process can be automated and visualized without a massive IT overhead.

    Key Technical Pillars

    1. Automated ETL Pipeline (The Engine)
      Built with a custom Python engine that autonomously fetches data from multiple financial APIs, cleanses it via Pandas, and stores it in a structured SQL environment.

    Lean Impact: 100% reduction in manual data retrieval time (Eliminating Waiting).

    1. Analytical Intelligence (The Logic)
      Implementation of advanced statistical measures and leading/lagging indicators. It identifies correlations between macroeconomic trends and specific portfolio assets.

    Lean Impact: Moves stakeholders from “What happened?” to “Why did it happen?” (Reducing Defects in decision-making).

    1. Interactive Decision Support (The Interface)
      A responsive UI built with Plotly Dash and Bootstrap. It allows for instant “drill-down” capabilities into granular data points with zero lag.

    Lean Impact: Single-screen visibility replaces multi-tab spreadsheet navigation (Optimizing Motion).

    Technical Stack
    Backend: Python (Pandas, NumPy, SQL)

    Frontend: Plotly Dash (Reactive Web Framework) & Bootstrap CSS

    Deployment: Cloud-hosted on Render

    DevOps: CI/CD via GitHub Actions for automated integrity checks

    Freelance Application
    I help operations-heavy businesses (Logistics, Manufacturing, and Finance) turn messy reporting processes into automated, high-visibility tools. This project serves as a live demonstration of my ability to:

    Connect disparate data sources.

    Build resilient, automated pipelines.

    Design intuitive interfaces for non-technical stakeholders.

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