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Professional Profile

About Me

I'm a Geospatial Systems Architect at Oak Ridge National Laboratory, where I transform research and experiments into production-scalable solutions. My work sits at the intersection of geospatial data, cloud infrastructure, and distributed systems—building the pipelines, architectures, and tools that make complex data problems tractable.

I specialize in taking theoretical concepts and making them work reliably at scale, whether that's designing GeoParquet data warehouses, architecting real-time IoT tracking systems, or building resilient distributed systems that handle failure gracefully.

What I Work On Now

At Oak Ridge National Laboratory, I'm currently focused on:

  • GeoParquet Data Warehouses: Architecting production-scale geospatial data warehouses with efficient partitioning, predicate pushdown, and S3-native query patterns
  • Decision Support Tools: Designing both web and desktop applications for risk analysis and geospatial decision-making
  • Real-Time Geospatial Tracking: Building IoT/Kafka/TimescaleDB pipelines for geospatial tracking on AWS
  • Raster Processing Pipelines: Maintaining large-scale raster databases with zonal aggregation, governed by Prefect workflows
  • Time-Series Infrastructure: Designing clock-drift time-series ingestion and analysis stacks

Core Domains

My expertise spans several interconnected domains:

🗺️ Geospatial Systems

  • PostGIS, GeoPandas, GeoParquet, QGIS
  • Spatial indexing strategies, raster-vector workflows
  • Large-scale geospatial data warehousing
  • Real-time geospatial tracking and visualization

☁️ Cloud Architecture & Infrastructure

  • AWS, GCP, Azure with focus on scalable data pipelines
  • Kubernetes (RKE2), Rancher, container orchestration
  • Infrastructure as Code (Ansible, Terraform)
  • Air-gapped and hybrid cloud deployments

🗄️ Data Engineering

  • ETL/ELT pipelines (Prefect, Airflow, Kafka)
  • Database design and optimization (PostgreSQL, TimescaleDB)
  • Data lake and warehouse architectures
  • Real-time data processing and streaming

🔒 Systems Architecture & Operations

  • Release management and progressive delivery
  • Configuration governance and secrets management
  • IAM/RBAC patterns for distributed systems
  • System resilience, rate limiting, and backpressure
  • Observability and monitoring (Grafana, Prometheus, Loki)

🤖 Machine Learning & AI

  • ML model deployment (ONNX, MLflow)
  • Feature engineering and data pipelines for ML
  • LLM integration and agentic systems
  • Model versioning and progressive rollout

🐍 Full-Stack Development

  • Python (FastAPI, NiceGUI, async patterns)
  • JavaScript/TypeScript (React, modern web)
  • Go and Rust for systems programming
  • API design and microservices architecture

Professional Experience

Geospatial Systems Architect

Oak Ridge National Laboratory | Oak Ridge, TN | 2023—Present

Leading architecture and implementation of production-scale geospatial systems, data warehouses, and decision support tools. Focus on reliability, scalability, and maintainability.

Senior Software Engineer — Data Engineering

Bold Penguin | Columbus, OH (remote) | 2022—2023

Centralized CI/CD infrastructure, led Prefect 2 migration, built scalable microservices and REST APIs, automated deployment pipelines.

Data Engineer

Oak Ridge National Laboratory | Oak Ridge, TN | 2019—2022

Rebuilt brittle ETL into Kubernetes/Airflow, containerized services, led COVID-19 tracking systems, chaired SQA board.

How to Read This Site

This documentation is organized into two main categories:

📖 Best Practices

Conceptual guides, patterns, and reference material for production systems. These are deep dives into architectural decisions, design patterns, and methodologies. Start here if you want to understand why and how to build systems a certain way.

Key Sections: - Architecture & Design — System design patterns, ADRs, caching, secrets management - Operations & Monitoring — Release management, configuration, resilience, observability - Security — IAM/RBAC, secrets governance - Database & Data — Postgres, PostGIS, data engineering patterns

🛠️ Tutorials

Step-by-step, hands-on implementation guides with copy-paste examples. These are practical walkthroughs for implementing specific technologies or solving concrete problems. Start here if you need to do something right now.

Key Sections: - Database & Data Engineering — PostGIS, Postgres, data pipelines - Docker & Infrastructure — Kubernetes, RKE2, containerization - Python Development — FastAPI, NiceGUI, async patterns - Machine Learning & AI — ONNX, MLflow, LLM deployments

🎨 Just for Fun

Creative & experimental projects that explore the edges of what's possible—from WebGL art with PostGIS rasters to Redis Streams + Web MIDI music systems.

Contact & Collaboration

I'm always interested in discussing: - Geospatial data engineering challenges - Distributed systems architecture - Production reliability patterns - Open source collaboration

Get in touch: - Email: jngrant@live.com - GitHub: @sempervent - LinkedIn: Joshua N. Grant - Blog: Not Just a Datum

Contact & Collaboration Guide →

Education

Master of Science, Plant Sciences — Plant Molecular Genetics
University of Tennessee | Spring 2017 | GPA: 3.72/4.0

Bachelor of Science, Plant Sciences — Biotechnology
University of Tennessee | Spring 2014 | GPA: 3.74/4.0 — Magna Cum Laude

Yes, I have a biology background. The transition to systems architecture came through data science, where I learned that building reliable data pipelines requires the same careful observation and systematic thinking as experimental science.