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When Michael Meyer told his high school guidance counselor he was skipping university to attend a trade school for programming, the response was blunt: “All the computer programs that have ever needed to be written have already been written. You’re going down the wrong path.”
That was the late 1980s. Michael ignored the advice, enrolled anyway, and spent the next 35 years building a career across programming, data architecture, product marketing, and solutions engineering. He’s currently a Solutions Engineer at Snowflake, helping enterprise customers in Omaha get more out of their data platforms.
Today, a new generation of data professionals is hearing a familiar refrain. AI will write your code. AI will build your dashboards. AI will make your job obsolete. I recently sat down with Michael on the Data Faces podcast, and his career offers a compelling counterargument. The skill that carried him through every industry shift was something most people in data overlook entirely. Storytelling.
“The writing about the craft beer is more about the people, the historical significance of places and things like that, than it is about the beer.” — Michael Meyer, Solutions Engineer, Snowflake
About Michael Meyer
Michael Meyer is a Solutions Engineer at Snowflake, where he supports enterprise customers across the Omaha, Nebraska market. His career spans over 35 years in programming, data architecture, data governance, and product marketing at companies including Alation. He is also the author of Joe’s Brew Reviews, a book about Nebraska’s craft beer scene that’s really about the people and stories behind the breweries. In our conversation on the Data Faces Podcast, we discuss:
Why storytelling is the most durable skill in a data career
How the semantic layer went from a BI footnote to AI’s missing piece
What vibe coding gets right and where it falls short
The fundamentals that early career data professionals need to focus on
A storyteller who speaks data
Michael’s relationship with storytelling started at home. His father was a logical thinker who passed down an analytical mindset, while his maternal grandfather was the kind of natural storyteller you’d sit and listen to for hours. Michael found himself torn between journalism and programming after high school. He chose programming, but the storytelling instinct never went away.
On one side of the family, he got the analytical instinct, and on the other, the urge to make people lean in and listen. That combination connects every role he’s held, from programming to data architecture to product marketing to solutions engineering. Each demanded a different technical skill set, but each rewarded the ability to make complex things understandable. Storytelling wasn’t a hobby Michael kept separate from his day job. It became the thing that made every job better.
Walt the data janitor and the power of internal marketing
When Michael transitioned into data governance at a financial services company, the team needed people across the organization to care about data quality, catalogs, and standards. Instead of frameworks and compliance language, they created a fictional character named Walt.
Walt showed up across the entire data management program. When the team discussed data quality, Walt was the janitor pushing a broom through messy datasets. When they introduced data architecture concepts, Walt played a different role. He gave people a relatable way into abstract subject matter, and it worked.
“We built up our data program from creating a fictional character that helped us show each step of the way, from data architecture to quality. His name was Walt. When we talked about data quality, Walt was the janitor pushing the broom through.” — Michael Meyer, Solutions Engineer, Snowflake
That experience crystallized something for Michael. Getting people to care about data is a communication challenge, not a technical one. His internal marketing work caught the attention of Alation’s field marketing team, and they recruited him into a technical product marketing manager role.
The mindset shock of marketing
Moving into product marketing forced a shift in how Michael communicated. The real breakthrough came from learning to listen for the exact phrases customers used to describe their problems and then putting those words on the page rather than his own.
“There would be key phrases that would come out that customers would say, and if you could use those, especially within what you’re trying to portray, that’s where you could get the hook. You’d better get them interested right away. How do you get to that emotional side of somebody so that they think, ‘Wait a minute, that’s me’?” — Michael Meyer, Solutions Engineer, Snowflake
That customer-language instinct turned out to be portable. Michael is now back on the technical side as a Solutions Engineer at Snowflake, where he helps key enterprise accounts in the Omaha market. His marketing background makes him better at translating a complex platform into terms that resonate with the people who actually use it. Marketing gave him a vocabulary for the rest of his data career.
The semantic layer is a storytelling problem
If you’ve worked in data long enough, you remember defining semantic layers inside BI tools like Cognos Framework Manager, giving human-readable names to cryptic database columns so business users could build their own reports. Nobody called it storytelling at the time, but that’s exactly what it was.
The concept never went away, but it stayed fragmented. Every BI platform maintained its own semantic definitions, which meant every tool told a slightly different version of the truth. AI changed the equation. When a business user asks a natural language question of an AI assistant, that assistant needs to understand the question in the terms the business actually uses, not technical column names or another company’s jargon, but the specific vocabulary of that organization.[1]
A modern semantic layer provides that context by defining facts, dimensions, metrics, relationships, and business rules in a format that both humans and large language models can interpret. Without it, AI tools produce answers that sound plausible but miss the mark, and once business users lose trust, they rarely come back.
“If I’m going to talk with my data, I need to talk to it in terms of how the business speaks with the data, not technical terms, not how another financial company talks to theirs, but how my organization talks. That’s really the key.” — Michael Meyer, Solutions Engineer, Snowflake
Gartner predicts that organizations will abandon 60% of AI projects unsupported by AI-ready data through 2026.[2] Michael estimates that building a good semantic model today is still about 70% human work. AI can generate descriptions and suggest metrics, but it can’t replace the institutional knowledge of someone who has spent years working with the data. A subject matter expert knows why two fields that look similar mean very different things in practice.
AI-assisted coding and the proof-of-concept trap
“Vibe coding” is the practice of using AI to generate code from natural language prompts rather than writing it by hand. Michael has been experimenting with Snowflake’s Cortex Code and recently used it to build a machine learning pipeline from scratch. He fed it a retail dataset, picked a use case focused on detecting late delivery issues, and let it design the full pipeline in a notebook.
The first model returned about 50% accuracy. Michael knew that was unacceptable. He iterated with Cortex Code, adding feature engineering and additional training, until the model reached 85% accuracy. The critical skill in that process came down to judgment, knowing that 50% meant the model was broken and that 85% meant it was worth showing to a data scientist for validation.
AI-assisted coding compresses timelines and lowers the barrier to exploring new technical domains. But it’s not a substitute for the judgment that comes from understanding your data. Someone who can’t read the story the numbers are telling, who can’t look at a metric and know whether it makes sense in context, will eventually ship something dangerous.[3]
“If you don’t have any understanding of how to test and verify, and if you’re just taking everything AI does as being 100% accurate, that could quickly actually become a career ender.” — Michael Meyer, Solutions Engineer, Snowflake
What endures when everything else changes
Michael’s guidance counselor was wrong about programming in the late 1980s. The people saying AI will replace data professionals are making the same mistake today. The tools and platforms will keep changing, but the fundamentals don’t.
When I asked Michael what early career data professionals should focus on, he didn’t start with AI prompting or the latest framework. He started with data modeling. Understand what good data looks like. Learn how raw data becomes something a business user can actually consume.[4] Know how to test and validate results, because if you can’t verify what AI gives you, you have no business putting it in front of a decision-maker.
Michael pointed out that he’s met AI engineers who have never worked with data before, and that gap shows up when models need to connect to real business outcomes. As Thomas Davenport and Randy Bean argue in MIT Sloan Management Review, 2026 is the year the industry must shift from chasing AI hype to realizing actual enterprise value, and that shift depends on exactly the kind of foundational data skills Michael is describing.[5]
“Find something that energizes you, what are some of your strengths, and lead into those. It took me a long time to really make my writing part of my career. But it can be done.” — Michael Meyer, Solutions Engineer, Snowflake
Beyond the technical fundamentals, Michael’s advice is to get out from behind the screen. He runs meetups in Omaha and credits the networking he’s done over the past decade with keeping him grounded and energized. Be a constant learner, he says, but learn from people, not just platforms.
Not all programs have been written. And the stories haven’t all been told.
Listen to the full conversation with Michael Meyer on the Data Faces Podcast.
Based on insights from Michael Meyer, Solutions Engineer at Snowflake, featured on the Data Faces Podcast.
Podcast highlights
[0:00] Opening and introduction
[2:00] Michael’s 35-year career across programming, data architecture, and Snowflake
[4:00] Joe’s Brew Reviews, journalism vs. programming, and the storytelling instinct
[6:30] Walt the data janitor and how a fictional character made data governance relatable
[11:00] The mindset shock of moving from data architecture to product marketing at Alation
[14:00] Customer language, emotional hooks, and storytelling on a B2B web page
[17:00] Coming back to the technical side as a Solutions Engineer at Snowflake
[19:00] What the semantic layer is and why AI made it urgent
[23:00] Facts, dimensions, metrics, verified queries, and business rules in a semantic model
[25:30] Building a semantic model: 70% human work and why institutional knowledge matters
[28:30] Vibe coding with Snowflake Cortex Code and iterating from 50% to 85% accuracy
[32:00] Why early career data professionals should start with data modeling fundamentals
[34:30] Find what energizes you, get out from behind the screen, and be a constant learner
[35:30] Craft beer recommendations and closing
About David Sweenor
David Sweenor is the founder and host of the Data Faces podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.
With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.
Books
Artificial Intelligence: An Executive Guide to Make AI Work for Your Business
Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies
The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications
The CIO’s Guide to Adopting Generative AI: Five Keys to Success
Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence
The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success
Follow David on Twitter @DavidSweenor and connect with him on LinkedIn
[1] David Sweenor, “Your AI Doesn’t Have a Model Problem. It Has a Data Context Problem,” TinyTechGuides, February 24, 2026, hcansubject-matter ttps://tinytechguides.com/blog/your-ai-doesnt-have-a-model-problem-it-has-a-data-context-problem/
[2] Gartner, “Lack of AI-Ready Data Puts AI Projects at Risk,” Gartner Newsroom, February 26, 2025, https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk
[3] David Sweenor, “Data Lineage for AI: Why Truth Beats Hope in Banking,” TinyTechGuides, December 2, 2025, https://tinytechguides.com/blog/data-lineage-for-ai-why-truth-beats-hope-in-banking/
[4] David Sweenor, “From ‘AI-Ready’ to AI Reality: Why Actionable Data Strategies Beat Endless Planning,” TinyTechGuides, June 3, 2025, https://tinytechguides.com/blog/from-ai-ready-to-ai-reality-shane-murray-on-data-trust-and-why-action-beats-planning/
[5] Thomas H. Davenport and Randy Bean, “Five Trends in AI and Data Science for 2026,” MIT Sloan Management Review, 2026, https://sloanreview.mit.edu/article/five-trends-in-ai-and-data-science-for-2026/
