Product + Data Engineer / Serial Tinkerer
I am the founder of Qry, an API layer for the data warehouse. It lets data teams publish secure, read-only APIs from SQL they already trust, then serves that data from a separate delivery layer built for applications, internal tools, workflows, and agents. You can think of it like a CDN for the data warehouse.
Qry has a working prototype, and I am beginning conversations with potential design partners. I am especially interested in teams whose analytics engineers are already being asked to deliver modeled data beyond dashboards.
Learn more about Qry →Over the years, big companies and startups alike have hired me to help analytics become a strategic partner to the business rather than a help desk for report requests, which often required finding ways to work around the BI software already in place. After watching the same pattern recur and rebuilding some version of the solution roughly half a dozen times, I began thinking seriously about Qry a few years ago. The proliferation of agents is what makes the timing feel right now.
I came to data through operations, and I have always seen it as a Trojan horse for process improvement. Making data actionable may be a tired phrase, but it describes the work I care about because trusted data makes a process visible, gives people a shared definition of what is happening, and creates a practical way to improve how the work gets done.
Ultimately, I want us to achieve the original promise of business intelligence. Every person in a company should be able to understand how the business works, see how their work and decisions affect its outcomes, and act using the same trusted model of the organization. Traditional BI made the business visible, but it still asked people to find the right dashboard, interpret it, connect it to the work in front of them, and figure out what to do next.
This time is different. The modern data stack has assembled a richer model of the business, and agents can bring the relevant part of that model into the moment a decision is made. They can help people understand what is happening, why it is happening, which levers they control, and whether their actions worked.
That's why Qry starts with the missing delivery path, turning trusted SQL and dbt models into production interfaces that software and agents can reliably use.
The General Business Intelligence thesis lays out the broader idea.
Read the GBI thesis →Founder & Technical Lead
Built data-intensive products and developer infrastructure end to end, including FastAPI and DuckDB-backed data services, multi-tenant SaaS systems, deployment automation, and production observability. Qry is the current focus.
Senior Analytics Engineer
Developed dbt models in Snowflake and customer-health logic that gave Scaled Customer Success visibility into roughly 40% of managed revenue exposed to churn risk. Connected those signals to operational workflows across thousands of accounts.
Solutions Engineer
Turned complex enterprise data integrations into repeatable Python tooling and ingestion patterns, automating about 70% of manual integration work and reducing customer time-to-value by roughly 50%.
Senior Analytics Engineer
Built event-driven reporting and data-quality workflows with dbt, Snowflake, Airflow, Python, and the Looker API. Write-Audit-Publish controls reduced downstream reporting errors from stale or bad production refreshes by 85%.
Analytics Engineer, Marketplace Demand Lead
Led reporting strategy and self-service BI rollout for more than 70 stakeholders. Refactored investor reporting into modular dbt models, reducing monthly generation time by 90%.
Founding Data Engineer
Joined as the first data hire and built the modern data stack for a 40-person Series A company, covering executive reporting, finance, marketing, inventory, customer cohorts, and operational analysis.