Bryan Mowrey

Data science · backend systems · analytics

Data systems built for real use.

I hold a degree in Data Science and build practical systems that are designed to be run, inspected, and evaluated—from SQL analytics to deployed Python APIs.

Explore selected work View GitHub
2Azure production systems
40SQL case studies
CIValidated releases

Selected work

Projects with working software behind them.

Each project includes a runnable path, visible output, and the implementation details needed to inspect how it works.

CivicOps ML production system: creation-time data, verified model, human review, Entra roles, Neon audit storage, and Azure monitoring

A deployed machine-learning system for authenticated NYC 311 resolution-risk review, with verified artifacts, required human decisions, and durable audit records.

Production app Model card Source
DataOps Observability API operations system: pipeline runs, quality checks, alert delivery, ownership, and Azure deployment

A deployed FastAPI operations service for pipeline runs, data-quality checks, webhook alerts, metrics, ownership, and operational runbooks.

Live dashboard API docs Source
SQL Mini Challenges analytics practice: forty challenges, validated outputs, four learning paths, and a SQL code workbench

Forty validated SQL case studies covering cohorts, retention, revenue, window functions, date spines, sessionization, and data-engineering patterns.

Project site Source

04 / Data Engineering

Data Engineering Lab

Reproducible CSV and API ingestion pipelines that clean, validate, load SQLite, execute analytics SQL, and produce inspectable reports.

  • Python
  • pandas
  • SQL
  • SQLite
View repository

05 / Full Stack

SignalBoard

A multi-tenant productivity dashboard with authenticated workspaces, task workflows, activity history, database models, and browser-level tests.

  • Next.js
  • TypeScript
  • Prisma
  • PostgreSQL
Live app Source

How I build

Useful output, supported by evidence.

01

Clear data paths

Inputs, transformations, storage, and outputs are explicit enough to follow from a clean checkout.

02

Automated validation

Tests, type checks, migrations, container checks, and CI gates establish that the documented path works.

03

Operational visibility

Health checks, metrics, alerting, release history, and runbooks make the software inspectable after deployment.

About

Practical engineering grounded in data.

My background in Data Science shapes how I approach software: define the question, make the data path visible, test the result, and leave behind something another person can run and evaluate.

I work across machine-learning systems, SQL analytics, Python services, data pipelines, and full-stack applications, with an emphasis on reproducibility and production readiness.

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