Chris Gambill
Founder & Principal Advisor, Gambill Data | Data Strategy, Architecture & AI Readiness
Knoxville, Tennessee, United States
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Chris Gambill is the founder and principal advisor at Gambill Data, a founder-led data strategy and architecture advisory firm. With 26+ years of experience across data engineering, governance, architecture, analytics, migration, and leadership, including 14 years in Fortune-scale telecommunications—he helps organizations make high-stakes decisions about data platforms, modernization, operating models, and AI readiness.
His work connects executive priorities with engineering reality. Chris advises leaders on Databricks and Microsoft Fabric strategy, lakehouse and data architecture, governance, platform reliability, and the organizational practices required to turn a roadmap into systems people can trust.
Chris is a Databricks Certified Data Engineer Professional. Gambill Data is a Databricks Bronze Partner and a member of the Microsoft AI Cloud Partner Program. He also coaches data engineers in technical judgment, communication, architecture, and leadership.
His sessions are practical, candid, and grounded in the production lessons that rarely appear in tutorials.
Area of Expertise
Topics
Modernizing Your Databricks Engineering: Using Lakeflow & Declarative Pipelines
The data landscape isn’t slowing down. Cloud platforms evolve, AI raises the bar, and modern data stacks demand engineers who can build pipelines that are scalable, cost-efficient, and resilient. This full-day hands-on workshop takes you from fundamentals to production-ready practices using Lakeflow Declarative Pipelines (formerly known as DLT) plus Databricks orchestration tools.
You’ll learn how to ingest and transform raw data with Lakeflow, orchestrate and monitor workloads, and apply real-world optimization techniques that save time and money. By the end of the day, you’ll have built your own end-to-end pipeline on Databricks and walked away with frameworks you can apply immediately to your organization’s projects.
What You’ll Learn
-How to design and implement Medallion‐architecture pipelines using Lakeflow Declarative Pipelines.
-How to orchestrate, schedule, and monitor your workloads in Databricks.
-Techniques for data quality, schema enforcement, and governance.
-Optimization patterns for performance and cost savings in the cloud.
-A framework for evaluating new tools and practices in a rapidly changing, AI‐driven world.
Format
Length: Full day (6.5 hours, including breaks)
Skill Level: Intermediate (familiarity with SQL or Spark recommended)
Modern Data Engineering with Lakeflow Declarative Pipelines & Databricks Orchestration
Cloud platforms continue to evolve, AI raises the bar, and modern data stacks demand engineers who can build pipelines that are scalable, cost-efficient, and resilient. The one that covers all this and is fastest to the finish line wins. This abbreviated hands-on workshop takes you from fundamentals to production-ready practices using Lakeflow Connect to get your data from source to Unity Catalog quickly and cheaply.
You’ll learn how to ingest raw data with Lakeflow, orchestrate and monitor workloads, and apply real-world techniques that save time and money. By the end of the session, you’ll have walked away with frameworks you can apply immediately to your organization’s projects.
What You’ll Learn
-How to design and implement Lakeflow Connect Pipelines.
-How to orchestrate, schedule, and monitor your workloads in Databricks.
-A framework for evaluating new tools and practices in a rapidly changing, AI‐driven world.
Adapt or Be Automated: Continuous Learning in the Age of AI and Data Engineering
In data engineering, the tools never stop changing. From on-premises ETL to cloud-native pipelines, from dashboards to AI-driven insights, the landscape evolves faster than most teams can keep up. But the data engineers who thrive aren’t the ones who know a single tool inside out; they’re the ones who adapt, learn, and apply new practices as the field transforms.
In this session, we’ll explore why adaptability is the most critical skill in the AI era. You’ll learn how to evaluate new technologies, when to embrace the latest innovations (like generative AI for pipeline automation), and when to stick with proven practices. Real-world stories from 25 years in the field will highlight how continuous learning turned potential failures into successful, future-ready data projects, and has kept me in a career that I love.
Agenda (60 minutes)
The only constant: change in data engineering (5 min).
A short evolution tour (10 min): from DTS → SSIS → ADF → Databricks & Fabric.
How AI changes the stakes (15 min): automation, copilots, and the importance of the "human in the loop"
Frameworks for adaptability (15 min): evaluating trends vs. hype, choosing what to learn.
Habits for continued growth (10 min): sustainable learning routines that fit busy engineers.
Q&A and audience stories (5–10 min).
Key Takeaways
Adaptability: not a single tool, is the most valuable long-term skill.
How AI raises the bar for learning speed and breadth in data engineering. (Evolution from Stack Overflow to Chat GPT)
A practical framework for evaluating new tools without getting caught in shiny-object syndrome.
Habits and resources that make ongoing learning realistic and effective.
Adapt or Be Automated: Continuous Learning in the Age of AI and Data Engineering
In data engineering, the tools never stop changing. From on-premises ETL to cloud-native pipelines, from dashboards to AI-driven insights, the landscape evolves faster than most teams can keep up. But the data engineers who thrive aren’t the ones who know a single tool inside out; they’re the ones who adapt, learn, and apply new practices as the field transforms.
In this session, we’ll explore why adaptability is the most critical skill in the AI era. You’ll learn how to evaluate new technologies, when to embrace the latest innovations (like generative AI for pipeline automation), and when to stick with proven practices. Real-world stories from 25 years in the field will highlight how continuous learning turned potential failures into successful, future-ready data projects, and has kept me in a career that I love.
Agenda
The only constant: change in data engineering (5 min).
A short evolution tour (10 min): from DTS → SSIS → ADF → Databricks & Fabric.
How AI changes the stakes (15 min): automation, copilots, and the importance of the "human in the loop"
Frameworks for adaptability (15 min): evaluating trends vs. hype, choosing what to learn.
Habits for continued growth (10 min): sustainable learning routines that fit busy engineers.
Q&A and audience stories (5–10 min).
Key Takeaways
Adaptability: not a single tool, is the most valuable long-term skill.
How AI raises the bar for learning speed and breadth in data engineering. (Evolution from Stack Overflow to Chat GPT)
A practical framework for evaluating new tools without getting caught in shiny-object syndrome.
Habits and resources that make ongoing learning realistic and effective.
How Data Engineers Can Stop Copy-Pasting and Start Architecting with Agents
Let's be honest: most of us are using AI like a glorified StackOverflow. We copy a messy JSON payload, paste it into ChatGPT, ask for a Python schema, and then spend ten minutes fixing the hallucinated data types. If your AI workflow consists of juggling three different browser tabs and manually reviewing basic code, you’re missing the actual leverage of modern AI.
AI isn't going to take your job, but the data engineer who figures out how to automate their own grunt work is.
In this talk, we’ll move past simple prompt engineering and explore how to use the latest agentic frameworks to dramatically accelerate your daily data engineering workflows—from data exploration to code review loops. We’ll skip the marketing slides and dive straight into a live-coding demonstration of the cutting-edge open-source and data ecosystem tools you should be using right now:
Omnigent: The brand-new open-source "meta-harness" by Databricks. We’ll show how to orchestrate multiple tools (like Claude Code and Codex) under one roof to handle complex engineering tasks—like having one model plan a pipeline migration, another implement it in a sandbox, and a third cross-review the diff.
Databricks Genie: How to spin up AI data assistants to automate data profiling, documentation, and ad-hoc analysis, effectively cutting down your "can you run this query for me?" ticket queue to zero.
Whether you’re skeptical of the AI hype or just haven't looked at the ecosystem lately, come learn how to build an autonomous engineering partner that handles the tedious tasks so you can focus on system architecture.
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