Writing
Practical notes on data engineering, architecture, and technology judgment.
The writing is intentionally selective. It reflects the questions data teams face in practice: fundamentals, reliability, platform choices, and how to stay thoughtful when technology moves quickly.
AI-native data teams: what drives success and what leads to failure
Most teams call themselves AI-native without changing how data, governance, or delivery actually work. Here's what actually separates the teams that deliver durable value from those stuck in demo mode.
What executives actually need from a data leader
Executives don't need more dashboards or a longer tool shopping list. They need a data leader who creates trust, improves decisions, and builds scalable organizational leverage.
Where AI fits in the modern data stack
AI doesn't sit outside your data stack. It works best where trust, governance, and reliable foundations already exist. Here's how to think about AI as an extension of modern data capabilities.
How to reduce data platform complexity without slowing teams down
A good data platform does not eliminate complexity entirely. It contains complexity in the right places and removes it from everyday delivery.
Why most data teams are busy but not effective
Data teams often appear highly productive—shipping dashboards, fixing pipelines, responding to requests—yet struggle to move the business forward. The problem isn't effort; it's a system designed for activity rather than leverage.
What I look for in a modern data platform
A practical framework for evaluating data platforms based on reliability, cost, developer experience, governance, and speed to insight.
Data Warehouse, Data Lake, or Lakehouse? A pragmatic guide
A practical decision framework for choosing data architecture based on workloads, governance needs, and team maturity.
Become a Data Engineer: Master the Fundamentals
A guide for building the core technical judgment behind data engineering, from systems thinking to the practical foundations that make teams effective.
Taming the Data Pipeline Beast: Best Practices for Robust Data Engineering
Design patterns for data pipelines that are observable, maintainable, resilient, and ready for real organizational scale.
Surfing the Technological Tidal Waves: Embracing the Hype and Conquering FOMO
A measured approach to new technology waves, experimentation, and separating durable value from noise.