I don’t just list tools on my resume—I use them day-to-day to solve real problems in public health, research, and NGO projects.
This tab shows how my skills + tools work together in real systems.

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Advanced SQL & Data Modeling

I use SQL and modeling to make data trustworthy, fast, and easy to analyze.

• Designed star and snowflake schemas in BigQuery for the CDC Foundation – Bear River Health Department, modeling programs like behavioral health, environmental health, and maternal/child health.

• Created fact and dimension tables with clear keys, conformed dimensions, and partitioning strategies so dashboards stay fast even with millions of records.

• Wrote complex SQL (CTEs, window functions, stored procedures, nested queries) in SQL Server / Azure SQL (Edgerock), BigQuery, and MySQL (NGOs) to support KPIs, exports, and operational reports.

• These skills let me bridge raw data and clean, warehouse-ready structures that analytics teams can trust.

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Cloud & Data Platforms​

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Azure Data Platform

Tools: Azure Data Factory, Data Lake, Synapse, Azure SQL, Functions, Key Vault, Automation, Databricks

• Migrated and modernized ETL from SSIS into Azure Data Factory (ADF).

• Landed data in Azure Data Lake and Synapse for analytics and reporting.

• Used Azure Functions to run Python cleansing jobs (CSV cleanup, XML → JSON, parquet compression).

• Managed secrets and secure access through Key Vault, and automated schedules/alerts via Azure Automation.

• Used Databricks concepts where needed for scalable analytics on top of lake/warehouse data.

• This gave me a solid foundation in building end-to-end, cloud-native data flows on Azure.

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AWS

Tools: S3, RDS (MySQL), EC2, Glue (concepts), IAM

• Hosted relational databases on AWS RDS (MySQL) for youth tracking and operational data (e.g., NGO projects like Mesa Farm and Big Brothers & Big Sisters).

• Used S3 for backups, file intake, and data exports.

• Deployed workloads on EC2 where needed for custom processing and APIs.

• Applied concepts from my AWS Solutions Architect certification to design scalable, secure environments (IAM roles, security groups, network access patterns).

• Worked with AWS Glue concepts for cataloging datasets and designing ETL patterns, even when the final implementation ran in Python or other orchestration tools—so architectures are Glue-ready when teams want to scale further on AWS.

• Even when not building huge clusters, I follow the same best practices you’d expect in an enterprise AWS setup.

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Google BigQuery

• At the CDC Foundation – Bear River Health Department (BRHD), BigQuery is the core of the warehouse.

• Designed fact and dimension tables for public health programs (behavioral health, environmental health, maternal/child health, and more).

• Built ELT patterns (Apache NiFi → staging → warehouse) and optimized queries with partitioning and clustering.

• Powered KPI dashboards in Metabase, Power BI, and Superset directly from BigQuery, including public-facing visualizations where appropriate.

• Most of my modern warehouse work lives in BigQuery today.

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ETL / ELT & Data Pipeline Design

I specialize in making data pipelines that are reliable, observable, and simple to maintain.

• At CDC Foundation, used Apache NiFi to build parameterized flows with clear routing, error queues, and logging from raw files/APIs into BigQuery.

• Applied data quality checks in NiFi (schema, volume, required fields) before data lands in the warehouse so bad files are caught early.

• At Edgerock, designed ETL using SSIS and Azure Data Factory, including migration of legacy jobs into modern, cloud-based patterns.

• Built Python-based jobs to clean, standardize, and transform data, making sure pipelines are rerun-safe, logged, and easy to debug.

• My focus is always: Will this still make sense to someone new a year from now?

• ​For collaboration and data sharing across program teams, I’ve used Microsoft SharePoint to centralize files, track ETL-related docs, and keep data flows aligned across departments.

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Databases & SQL Engines

I’m comfortable across multiple database engines:

• SQL Server / Azure SQL at Edgerock for OLTP + reporting: stored procedures, views, triggers, and performance tuning using SSMS and Profiler.

• MySQL (often on AWS RDS) at Mesa Farm and Big Brothers & Big Sisters, designing schemas for youth, mentors, attendance, and program data.

• Cosmos DB at CDC Foundation as part of a broader health data ecosystem, where flexible JSON structures help bridge between transactional data and analytics needs.

• I’m used to modeling data, enforcing integrity, and writing complex SQL with CTEs and window functions.

• I’ve also engineered and managed analytics setups using Microsoft Access with SQL and advanced Excel functions, building relational tables, complex queries, and automated reports for program monitoring.

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Analytics, KPIs & Storytelling

I turn data into clear stories that non-technical stakeholders can act on.

• At CDC Foundation/BRHD, translated program questions (e.g., “Are we reaching the right clients?”) into KPI dashboards in Metabase, Superset, and Power BI.

• For Mesa Farm and Big Brothers & Big Sisters, built dashboards for attendance, mentoring sessions, and engagement that staff could understand in minutes.

• At UNT AIM-AHEAD, used Tableau / Power BI to visualize research outcomes, helping clinicians see trends, disparities, and model results clearly.

• I always start with: What question is this chart answering, and for whom?

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Data Quality, Governance & Documentation

I care a lot about making data reliable and explainable.

• Built a Data Quality Agent (Data Agentic AI) using Python, Pandas, SQL connectors, and LLM APIs (OpenAI, Gemini) to automatically profile files, detect issues (nulls, duplicates, invalid values), and suggest or apply fixes.

• Documented data contracts, field definitions, and known limitations so people know what each metric means and how it was calculated.

• Implemented QA steps in ETL (row counts, schema checks, freshness thresholds) so issues are caught before they ever reach leadership dashboards.

• For me, good data governance means: clear rules, clear documentation, and predictable behavior.

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Machine Learning & AI Thinking

I use ML and AI where they genuinely add value, not just as buzzwords.

• At UNT, built models like Gradient Boosting and OLS regression to study cancer outcomes, focusing on interpretability and fairness.

• At CDC Foundation, applied LLMs (OpenAI, Gemini) not to replace humans, but to:

  • find unusual patterns,

  • propose data fixes, and

  • generate plain-language explanations of data quality issues.

• I always ask: Can the person using this understand what the model is doing and why?

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Architecture, APIs & System Design

I like designing systems end-to-end so every piece fits together.

• For CDC Foundation/BRHD, helped shape a modern architecture: sources → NiFi → BigQuery warehouse → dashboards (Metabase / Power BI / Superset) → public/private views.

• In NGO projects, designed REST APIs (Node.js) and mobile flows (Flutter) that connected field staff, data stores, and analytics layers.

• I map systems visually using Visio and Excalidraw so stakeholders can see how data moves and where decisions are made.

• Architecture is where I ensure the system is not just working—but scalable, secure, and understandable.

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Programming, AI & Automation

• Python (Pandas, NumPy, Visualization, ML)

  • Used for Data Quality Agent logic, ETL scripts, EDA, survival analysis, model development, and utilities running in Azure Functions or on servers.

• Node.js & Flutter

  • Built Node.js backends to handle APIs, authentication, and data access for NGO mobile apps and dashboards.

  • Used Flutter to build cross-platform apps for youth check-ins, mentor scheduling, geofencing, and push notifications.

  • This gave me end-to-end experience from database → API → mobile UI.

• I use automation wherever it can reduce manual work while keeping behavior transparent.

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APIs & Integrations

I’ve integrated with:

• REST APIs for data ingestion, sync, and external services.

• Google Sheets API to connect lightweight admin workflows with more robust backends.

• SFTP and cloud storage (Azure, AWS) for automated file-based data exchanges.

I design APIs and integrations with clear contracts and logging so they’re predictable and maintainable.

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Analytics & BI Tools

• Power BI

  • Used at Edgerock for executive and operational dashboards built on Azure SQL and warehouse data.

  • At CDC Foundation / BRHD, turned BigQuery data into program KPIs and executive summaries.

  • Handled data modeling, DAX measures, row-level security, and publication to Power BI Service.

• Tableau, Metabase & Apache Superset

  • At UNT AIM-AHEAD, used Tableau to visualize research datasets (outcomes, distributions, survival curves).

  • At CDC Foundation / BRHD, use Metabase and Superset to:

    • Publish internal and external dashboards.

    • Build standardized KPI views for behavioral health, environmental inspections, and more.

    • Provide click-through paths from high-level metrics down to record-level details.

• Excel

  • Quick data checks and reconciliation.

  • Prototype analyses before turning them into formal reports.

  • Export layers for stakeholders who prefer spreadsheets over dashboards.

I treat Excel as a bridge between raw data, pipelines, and polished BI.

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Stakeholder Communication & Leadership

I’m comfortable in rooms with engineers, executives, clinicians, and community staff.

• At CDC Foundation, worked with epidemiologists, program managers, and IT to align dashboards with real-world workflows.

• In NGO work, led volunteer technical teams and translated mentor/teacher needs into features, KPIs, and workflows.

• I regularly run demos, training sessions, and feedback loops, making sure people trust the data and feel ownership of the systems.

My goal is always to make tech feel like a partner, not a barrier.

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Testing, UAT & Documentation

I treat testing and documentation as core parts of engineering, not afterthoughts.

• Wrote test cases for ETL jobs, SQL logic, and dashboards at Edgerock and CDC Foundation, validating row counts, transformation rules, and business logic.

• Supported UAT with business users, capturing their feedback and updating logic or visuals to match their mental models.

• Created runbooks and troubleshooting guides so others can support and extend what I build.

This reduces risk and makes it easier for teams to maintain the work over time.

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Security, Compliance & Privacy Mindset

Working in public health and with youth data means privacy is non-negotiable.

• At CDC Foundation, aligned work with HIPAA-style controls: least-privilege access, PHI separation, encryption, and logging.

• In NGO projects, followed CCPA-like principles, carefully handling personal data for youth and families.

• I design systems where sensitive data is limited, masked, or aggregated whenever possible.

I think of security as an integral design constraint, not a feature bolted on at the end.

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Agile, Collaboration & How I Work Day to Day

I’m used to working in Agile teams and distributed environments.

• Used JIRA and Confluence at Edgerock and mirrored similar practices at CDC Foundation for sprint planning, standups, and retros.

• Collaborated via Git / GitHub / Bitbucket, code reviews, and (where applicable) CI/CD-style pipelines.

• Stay close to users and iterate quickly—shaping dashboards, pipelines, and features based on real feedback, not assumptions.

I try to be the kind of data engineer/analyst people enjoy collaborating with, because they feel heard and supported.

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Operating Systems & Scripting

I’m at home in both Windows and Linux.

• Use shell scripts for file transfers, cron-like scheduling, and housekeeping tasks.

• Manage environments for Python, Node.js, and database tools.

• This helps me debug issues end-to-end—from OS and network up through the pipeline and dashboard.