Ship Data, Not Just Code: A Software Engineer’s Intro to dbt and DataOps

You already know how to build software. This workshop shows you how to apply that engineering mindset to data.

Designed for software engineers who are curious about data engineering, this hands-on session introduces the core building blocks of modern analytics engineering and DataOps, with dbt as the centerpiece. We’ll start with the big picture — how raw application data becomes trustworthy, usable datasets — then work through the practices that make data systems maintainable: modeling, testing, documentation, version control, code review, deployment, and observability.

Along the way, we’ll compare familiar software concepts to their data equivalents: services vs. pipelines, CI/CD vs. DataOps, unit tests vs. data quality tests, and application schemas vs. analytics models. You’ll see where software engineering instincts help, where they need to adapt, and why dbt has become such a powerful tool for bringing software-style discipline to the data stack.

By the end of the workshop, attendees will have:

* a practical mental model of modern data engineering and analytics engineering
* hands-on experience building and testing transformations with dbt
* an introduction to DataOps concepts and workflows
* a clearer understanding of how software engineering skills transfer into data work

Whether you’re exploring a move into data engineering, collaborating more closely with data teams, or just want to understand what happens after the database write, this workshop will give you a grounded, practical way in.

From APIs to ETL: Learning to Think Like a Data Engineer

After twenty years in software engineering, I made the jump into data engineering and discovered that many of my instincts still worked — right up until they didn’t.

Some skills transferred cleanly: debugging, automation, systems thinking, and caring about reliability. But data engineering forced me to think differently about ownership, correctness, observability, and what it means for a system to be “done.” Building pipelines is not the same as building products, even when the tools and code look familiar.

In this talk, I’ll share the most important lessons from making that transition mid-career: the habits from software engineering that gave me a head start, the assumptions that caused the most pain, and the mindset shifts that helped everything click. If you’re curious about data engineering, considering a career pivot, or simply want a better mental model for how data systems work, this talk will give you an honest and practical view from someone who had to learn the differences the hard way.

Beyond the Prompt: Data Engineering for AI-Powered Applications

Building AI features is easy to demo and hard to operate. The real challenge usually isn’t the prompt — it’s the data.

This hands-on workshop is for software engineers who are building AI-powered applications and discovering that model calls are only the visible tip of the stack. Behind every useful AI feature is a data engineering problem: getting the right data, preparing it for downstream use, keeping it fresh, retrieving it reliably, and knowing when the system is quietly going off the rails.

We’ll walk through the core data patterns behind modern AI applications, including ingestion, transformation, chunking, enrichment, retrieval, evaluation, and observability. Along the way, we’ll look at the failure modes that show up after the demo works: stale context, weak retrieval, bad metadata, silent data quality issues, and pipelines that no longer match the shape of the application.

The goal is to give software engineers a practical mental model for the data layer of AI systems — and a clearer understanding of how DataOps practices like testing, versioning, monitoring, and traceability help turn AI features into production systems.

By the end of the workshop, attendees will have a grounded framework for thinking about AI systems as data systems, and a better sense of how to design, debug, and operate them in the real world.

Beyond the App: A Software Engineer’s Guide to How Data Engineering Really Works

Your application writes data somewhere — then what?

For many software engineers, data engineering is a vague but important layer of the stack: pipelines run, dashboards refresh, reports appear, and somehow the company keeps making decisions. This talk is a practical introduction to what data engineers actually do, how their systems differ from traditional application development, and why building reliable data platforms requires a different set of instincts than building services or APIs.

We’ll look at the core pieces of the job — ingestion, transformation, orchestration, modeling, and data quality — through the perspective of someone with a software background. Along the way, I’ll show where software engineering habits help, where they break down, and how to think more clearly about systems whose purpose is not just to serve requests, but to move and shape data over time.

If you’ve ever wondered what happens after the database write, this talk will give you a grounded mental model of the field and a clearer sense of why data engineering is harder — and more interesting — than it first appears.

Operationalizing AI Agents: Limiting risk on the bleeding edge

You've successfully built your first AI agent ... now what? How do you operationalize the agent, with appropriate guardrails, while accounting for security, data privacy, and governance? Process orchestration can help you operationalize AI in a way that will eliminate the siloed use of AI/ML tools and services; put your teams on the fast track to orchestrating your most business-critical processes; and help you uncover hidden value in your processes and drive continuous improvement.

In this talk, I will share how to:
* Create an automation strategy that starts where humans can shine and infuses generative AI throughout the process automation journey
* Establish AI governance with a focus on purpose, culture, assessment, and action
* Combine AI with process orchestration to deliver an automation fabric that’s flexible, robust, and intelligent

AlphaZero to Hero: Solving board games with AI/ML [Full-Day Workshop]

In December of 2017, DeepMind introduced AlphaZero, a machine learning algorithm that taught itself to play chess, shogi, and go. After only four hours of training, it was arguably the most powerful chess engine at the time.

Why not build your own???

No, seriously, let's do it! If you're like the rest of the world, you played at least a few games of chess after binge watching The Queen's Gambit. Whether you've played chess or not, it's much easier than you might think to create your own chess engine. And what better way to learn some AI/ML than to write a program that can beat any human on the planet in a game of chess?

We will start with a simpler, but related, problem to solve: Connect 4. This will lay the groundwork for the chess engine by examining step by step how to approach solving Connect 4 heuristically, then using deep learning to train a neural network to play the game. Once Connect 4 is solved, we will take what we learned and apply it to chess, working to train a neural network that teaches itself.

You should leave this workshop with a working algorithm to solve Connect 4 and chess, a better understanding of how AI/ML can be applied to solve problems, and, with any luck, some ideas on how to use AI/ML in your daily work.

How do chess engines work? A look at applied AI/ML principles

If you're like the rest of the world, you played at least a few games of chess after binge watching The Queen's Gambit. Beth Harmon didn't have powerful computers to help her train like we do today. Ever since Deep Blue famous defeated Gary Kasparov in 1997, chess engines have become a critical part of the game.

But how do chess engines work? Is it hardcoded from a database of known moves, or is it learned through training? Could it be using both?

More importantly, what can you learn from chess engines? You should leave this session with an understanding of how modern chess engines work, a better understanding of how AI/ML can be applied to solve problems, and, with any luck, some ideas on how to use AI/ML in your daily work.

Accessibility: A Walk in Someone Else’s Shoes

Everyone talks about accessibility - or a11y - but how often is accessibility a primary thought during your development pipeline? How often is accessibility taken for granted? It’s easy to push it aside and say you’ll do it later or to forget entirely. It’s easy to drop a couple WAI-ARIA tags into your HTML and move on, but this doesn’t address many accessibility needs. When was the last time you used your website with your eyes closed?

Let’s do just that. Let’s try to navigate a website with a blindfold on. Let’s try to use a website without a mouse. Let’s try navigating a website with a visual impairment. And then let’s fix the problems encountered.

We will focus on experiencing a website as a user with two types of impairments:
* Visual impairments, such as color blindness, low visual acuity, and a complete lack of vision
* Mobility impairments, preventing users from using a mouse for input

These impairments are quite common and are simple to simulate using a combination of browser extensions and existing tools in your operating system. For each impairment, we will look at how the markup (both the semantic structure and the attributes), the colors and contrast, tab order, and focus affect the experience. And for each issue we encounter, we will look at specific ways that experience can be improved.

Last, we will look at testing strategies to audit your code for potential accessibility issues, using extensions such as Google’s Lighthouse, Deque’s axe-engine, and others.

Previous workshops:
CodeMash - January, 2019
Music City Tech - May, 2019
THAT Conference - September, 2019

Empathy, chemotherapy, development: A journey

In 2016, my daughter started chemotherapy for a rare disease. Over the next two years of ups, downs, twists and turns, I’ve learned empathically listening to people is not something just for medical professionals, but for software teams as well. The parallels between patient support and working with clients or projects became clear. Naturally, it is easier said than done. Sharing stories from my personal experience, I will illustrate the common mistakes and how to turn them around. This is a talk for more than just developers or consultants; everyone can walk away with something new.

Previous presentations:
Dog Food Conference - October, 2018

The Cost of Accessibility: A Business Case for A11y

Accessibility is a growing concern in the developer community, but the question of "Why" still lingers in the air. Why should a developer care about accessibility? Why should a private business invest in accessibility? One answer is money.

In 2019, Federal Courts ruled that a pizza chain must make their web platform accessible in accordance with the Americans with Disabilities Act. That is just one of many recent rulings holding businesses accountable for accessibility. Not to mention that 1 in 5 visitors to your website will have some form of disability - that's 20% of your users! This isn't only for public facing websites, either. The ADA also requires internal websites to be accessible.

This session outlines the business case for accessibility - why it's important and what the ROI for accessibility could be - as well as presenting the possible costs associated with ignoring accessibility. Take the notes from this session, or take the slides themselves, and present them to your business team. Help make the web accessible to everyone!