Senior Data / Software Engineer

Matt Caddell

DevOps & Data Specialist, Avid Tinkerer, Technical Lead, Amateur Wrench Turner & Coffee Snob

Senior Engineer with several years of experience focusing on data processing, data warehousing, BI tool stability, and maintaining the services to fuel all of that.

Not a qualified front-end engineer; this site is based on a template from templatemo

Learn about me


Data Processing

Ensuring that data is delivered in a timely fashion from multiple sources and with the relevant transformations so it's ready to be used

Data Warehousing

Democratizing data access through appropriate warehouse design choices and data management architectures

"Data Ops"

The best data in the world is only useful if it's realiably delivered; data related technologies come with their own devops style challenges and considerations


Senior Data / Software Engineer

Both the business and tech side of every company has a growing need to access and form conclusions from all kinds of data, I help make that happen.

I have extensive experience doing with more traditional DevOps/SRE style work to optimize build and release pipelines, and maintain infrastructure across multiple cloud providers.

Attended the University of Illinois at Urbana-Champaign for undergrad, earning a Bachelor of Science in Computer Science with a specialty in "Big Data and Artificial Intelligence".

I tend to spend my free time sitting in coffee shops sipping coffee, in a garage wondering why I can't get a motorcycle to start, working on perfecting a new recipe, or spending time with friends enjoying a nice afternoon.

Years of Experience 5
Average Cups of Coffee / Day 3
Jobs Held 2
Number of Motorcycles in Garage 1


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It may be pricey, but the cloud agnostic aspect makes it easy to use Snowflake with any tech stack, and it scales with minimal effort.
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Apache Spark

After years of using standard MapReduce and Apache Crunch to write ETL pipelines, Spark has felt like a revelation.
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Apache Airflow

You can run it anywhere, on anything, and accomplish almost any data related task with out of the box tools. The recent ability to extend Airflow to add your own new plugins makes it easy to build the new features you need.
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Amazon Web Services

If there isn't an AWS service that can already do at least half of what you need, it may be worth questioning if it's a good idea to do it at this point...
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Having a well managed set of kubernetes clusters has made my ability to stand up and test drive new systems and services a non-issue, and it allows for scaling and cost sharing across multiple teams and systems that I've previously never had.
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It has made writing and running complex release pipelines easy and saved hours of manual effort for teams I've worked with.
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You want to deploy a complex system entirely in kubernetes in seconds? Done. With almost no hassle, and single digit number of commands. Being able to test new tools in minutes has made vetting them easier than ever.
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GitHub Actions

It makes sense to have you code tested and built as close to the tool you use to host and review it, and this makes it quick and simple to do that. Also, anything is better than an unmaintained Jenkins instance.