Work
Three bounded engagements, and the operating outcomes from the years before Data Outfit existed.
2-3 weeks
An independent, senior read on your data platform before you commit to a migration, a rebuild, a vendor, or a big reliability or cost bet. What is actually broken, what is expensive theater, and what you should leave alone.
Before a migration, a platform rebuild, a vendor decision, or any bet you will still be explaining in three years.
4-8 weeks
For teams that already understand the problem and need the target architecture and the path to get there: sequencing, tradeoffs, and the calls that have to be made before anyone opens a ticket.
Platform modernization, a warehouse or streaming migration, ClickHouse adoption, semantic architecture, or any transition that needs a real plan, not a slide.
Bounded · tied to the transition, not the calendar
Senior technical involvement through the dangerous part of a transition, not a permanent seat. Design reviews, implementation checkpoints, and direct decision support until the transition is through its risky phase. I help get you through the hard part. I do not become your ongoing management.
When the architecture is decided and execution has entered the phase where a wrong call is expensive: a migration in its risky window, an implementation going sideways, a team stuck on an architectural blocker.
Advisory. Not staff augmentation, not a body shop, not a reseller with a services arm. Fixed fee or a capped, bounded scope, agreed before work starts. Priced for the size of the decision, not the length of the deck.
Proof
Kustomer
The warehouse was governed. The surfaces people trusted were not. Same business question, different answers in-app, in BI, and in finance, because every tile re-implemented the metric in hand-written SQL. Governance stopped at the gold layer.
Closed the loop. A self-hosted MetricFlow serving layer over ClickHouse so app and BI query the same definitions under multi-tenant security. Then a nightly quality score and Certified badge that only lands when a metric has a ratified brief, a semantic definition, and proven provenance on the surface. Models advise on failures. They never assign the score.
Kustomer
No durable footprint under the metric story. Residency constraints, no lakehouse spine, no one on the pager, and a cloud bill that did not explain itself.
Made the investment case, then designed, shipped, and operated the platform (ClickHouse, Kafka, Iceberg, dbt, Airflow) through residency requirements. Stayed on-call for what shipped.
Zillow
Fragmented stacks and rising ETL cost were slowing product and ML. Ownership stayed unclear.
Unified data science, MLOps, and engineering on a lakehouse path, tightened governance as ownership decentralized, and put real-time clickstream on the product decision loop.
Kin
Custom-heavy batch paths were burning engineering capacity. Critical teams were stuck on stale data.
Hub-and-spoke data ownership, a move to modern lakehouse tooling, and real-time paths where stale batch was the constraint.
These are operating outcomes from inside those companies, not invented client logos. New advisory work stays off the website unless you ask to be named. Diligence is easy: LinkedIn, the quotes below, and ask for a live reference on the fit call.