Julie Heckman
Snowflake | Senior Solution Engineer
Kansas City, Missouri, United States
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I’ve spent nearly 20 years bridging the gap between petabyte-scale chaos and business-ready clarity. My journey started in the algorithmic trenches of Kansas City’s high-frequency trading scene and has since evolved through the complexities of Agricultural IoT, public sector digital transformation, and global enterprise data strategy.
From coding MapReduce and Spark algorithms at Tradebot to leading high-performing app dev teams through complex digital transformations, I’ve seen it all. Having navigated many migrations from on prem to the cloud and the "integration tax" of the modern data stack, I’ve developed a healthy immunity to architectural buzzwords and a pathological commitment to data provenance.
Currently, as a Senior Solution Engineer at Snowflake, I act as a bilingual interpreter between the C-suite and the dev team—translating executive visions into technical wins. I advocate for the Data Cloud because it turns a liability—data pain—into a strategic asset. I help organizations optimize their infrastructure so they can stop managing servers and start managing outcomes.
Area of Expertise
Topics
Stop the Metrics Madness: How a Semantic Layer Tames AI and BI
Why does the "Active Customer" count differ between your executive dashboard and your data scientist's notebook?
As we move into the era of AI Agents, the need for a Semantic Layer is critical. This session explains how to define business metrics (YTD Revenue, Churn, etc.) once in a YAML-based model so that both AI assistants (like Snowflake Cortex) and BI tools (like Tableau or PowerBI) speak the same language.
We will explore the architecture of "Semantic Views" and how to use verified queries to ensure AI doesn't hallucinate when answering business questions.
Kill the Zombies: Real-World Data FinOps War Stories and How to Win Them
Ever opened a cloud billing alert and felt your heart skip a beat? From the "SELECT *" query that cost more than a used car to the "zombie" data pipelines that haunt your budget long after a project is dead, cloud costs are the ultimate horror story for modern engineers.
In this interactive session (yes, I will be asking for crowd participation), we’ll move past the boring spreadsheets and dive into real-world "War Stories" of accidental $500 table scans and the "silent killer" of cross-region data taxes. You will learn how to hunt down "compute bloat," implement "circuit breakers" to stop runaway costs, and finally co-locate your data so your storage and compute are roommates instead of long-distance pen pals.
We will wrap up with a "Monday Morning Checklist" of 10 immediate actions that you can take back and implement immediately to start activating cost savings.
Be a Helper: The 2026 Evolution of the Data Engineer
For years, the data engineer was the unsung "janitor" of the tech world—spending 80% of their time scrubbing messy CSVs and fixing brittle pipelines with tools like Informatica Power Center and SSIS packages. But the tide has turned. In 2026, the rise of Agentic AI and Autonomous Data Operations has automated the "drudge work," moving the engineer from the boiler room to the architect's office.
This session explores how the role has evolved from building point-to-point pipelines to designing Context Systems. We’ll discuss why your new primary "customer" is an AI Agent and how that changes everything from schema design to metadata strategy. Using real-world examples we will map out the new technical landscape.
Whether you’re a lead looking to upskill your team or an engineer wondering if AI is coming for your job, this talk provides a roadmap for thriving in the era of the Autonomous Data Lifecycle. We’ll move past the "janitor" mindset and embrace a future where being a "helper" means building the governed, high-context foundations that make AI actually work.
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