Product Management
Idea to Shipped - I like owning the whole arc: figuring out why something matters, shaping what it should be, then staying with it until it ships and works for real customers.
Listening to Customers - Most of my best product calls have started in a customer conversation. I spend a lot of time with users, sales and solution architects, turning “this is painful” into something worth building.
A Career in Data - I’ve worked on data products my whole career: at Dow Jones and Bloomberg, on Power BI and Microsoft Fabric at Microsoft, at Binance Cloud, and now at PhoenixAI. The industries kept changing, but the work always came back to data.
Working with Engineers - My favorite part of the job is sitting with engineers until a fuzzy problem turns into something we can actually build. I also spend a lot of time with design, QA and go-to-market, keeping everyone pointed at the same outcome.
BI, Analytics & Data Lakes
Where It Started - I began in digital marketing and audience analytics, helping teams make sense of their data and grow with it. That is where I first got hooked on what data can do.
Business Intelligence - I spent years building the tools people use to understand their data, from reporting and dashboards to semantic models. The goal was always to give analysts and executives what they need without slowing them down.
Lakehouse & Architecture - Along the way I got more and more interested in the architecture underneath: lakehouses, real-time processing, SQL federation. I enjoy working with engineers on how these pieces fit together and why it matters to the business.
Moving Down the Stack - Each role took me a layer deeper: from analyzing data, to building BI products, to the platforms behind them, and now into the database engine itself.
AI & DATA PLATFORMS
Analytical Databases - These days I spend most of my time inside a distributed analytical database built on Apache StarRocks. Storage, compute-storage separation and real-time queries are where I live, and I care most about making fast things also boring and predictable in production.
AI Data Infrastructure - I’m curious about what a database should become when its main user is an AI agent rather than a person. That has led me into LLM observability, evaluation, and the context layers AI apps quietly depend on.
Cloud & Enterprise Platform - Cloud and BYOC deployments, and the unglamorous parts that make them trustworthy: disaster recovery, multi-region resilience, the things nobody notices until they need them.
Operability - I believe a good database should explain itself. I push for systems customers can understand and debug on their own, rather than having to file a ticket and wait.
Crypto & Web3.0
Web 3.0 - A long-running side interest from my time managing a few exchanges. I still keep an eye on DeFi, custody and the infrastructure underneath it all.