When the problem is bounded, but the stakes are not.
Some data-platform problems are too consequential to guess on and too bounded to justify a permanent hire: architecture calls, migrations, streaming systems, cost, and metric trust. I take those on directly, as the person who has actually operated systems like this before. Fifteen years inside Zillow, Kin, Pluralsight, Kustomer, and Disney/ESPN, remote across the US and Europe.
Most data-platform problems that actually keep a CTO up at night are not staffing problems. They are judgment problems: a migration with one real shot at sequencing, an architecture that has quietly outgrown its assumptions, a cost curve nobody can explain, a metric that means three different things depending on who you ask.
A company with an ongoing need for a Head of Data Platform should hire one. Data Outfit exists for the other case: a bounded, consequential decision that needs someone who has actually operated systems like this before, for exactly as long as the problem requires, and no longer.
02
Engagements
Three ways to bring me in.
Three ways to bring me in, all bounded, all fixed fee or capped. You work with me directly, start to finish.
Start here
Terrain Survey
2-3 weeks
Fixed fee · typically mid five figures
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.
A written assessment your engineering leadership and finance can both sit through, plus a prioritized set of recommendations you can act on immediately.
Before a migration, a platform rebuild, a vendor decision, or any bet you will still be explaining in three years.
An architecture and system assessment
The two or three risks that actually matter
What is worth fixing, and what to explicitly leave alone
A decision framework, not just a hunch
An executive readout and a technical readout
Route Plan
4-8 weeks
Fixed fee · scoped to the decision
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.
A target-state architecture, sequencing and milestones, build-vs-buy calls, and the risks and dependencies that will actually determine whether it ships.
Platform modernization, a warehouse or streaming migration, ClickHouse adoption, semantic architecture, or any transition that needs a real plan, not a slide.
Target-state architecture and the major decisions behind it
Sequencing and milestones
Build-vs-buy calls and tradeoffs
A dependency map and the risks that could blow up the timeline
Traverse
Bounded · tied to the transition, not the calendar
Scoped engagement, capped
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.
Regular design and architecture reviews, migration checkpoints, and technical decision support until the transition is stable.
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.
Scheduled design and architecture reviews
Migration or implementation checkpoints
Direct technical decision support when a call cannot wait
Limited hands-on prototyping, only when it unblocks the team faster than a review
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.
The architecture holds until traffic or data volume spikes, and nobody is sure what gives first.
02
The data-platform bill doubled. Finance is asking a question engineering cannot yet answer with a model.
03
Every team defines the same metric differently, and the dashboards have stopped being trusted.
04
There is a migration on the table that the business will live with for years, and one real chance to sequence it.
05
The streaming or event architecture has grown too complex for anyone to reason about with confidence.
06
AI is now querying the data, and nobody can prove the answers it returns are actually correct.
If two or more of these are familiar, it is worth a conversation.
04
Open source & research
In development
Analytics Agent Reliability Harness
An open-source harness for evaluating whether AI analytics agents return correct business answers, not merely syntactically valid SQL.
Modern analytics agents can generate SQL that executes cleanly and still returns the wrong business answer. The query runs, the chart renders, and the number is wrong in a way that is easy to miss and expensive to trust.
That failure mode does not show up in most evaluation approaches, which stop at whether a query is valid, not whether it is true.
Two problems I owned end to end. Numbers first. The same class of work I take on now.
01
Make every number safe to quote
Kustomer
1x
metric definition, every surface
18-30ms
parity overhead on the serving path
Certified
line between quote-safe and exploratory
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.
Write-once metrics for customer-facing reporting and internal BI
Multi-tenant RLS with byte-identical parity checks
Self-serve BI with certified finance, product, and CS surfaces
"Safe to quote" enforced by the platform, not a wiki
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.
One plane for science, MLOps, and engineering
Clearer ownership without weaker controls
Clickstream feeding product decisions in real time
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.
06
Who you work with
Hi. I'm Alex.
Comfortable in a code review and a budget meeting. Prefer neither to be theater. This is a founder-led practice, not a firm with a bench.
Alex Silva
Engineer · Operator · MBA
I've spent fifteen-plus years inside companies where the data plane is the product: Zillow, Kin, Pluralsight, Kustomer, Disney/ESPN, and a few before that. I like the unglamorous work: making the bill make sense, making the metric mean one thing, making a migration survive contact with production. I also have an MBA from UCF, which mostly means I can sit with a CEO or a CFO without turning into a different person. Data Outfit is me, on purpose, for problems that are bounded and consequential, not for roles that should be filled by a permanent hire. No bench. No partner logo wall. If the work needs a squad of contractors or a full-time executive, I will tell you that on the call and wish you luck.
“If you cannot explain the tradeoff to the people funding it, the architecture is not done.”
MBA
University of Central Florida
15+
years building data platforms
10-50+
engineers led per organization
What colleagues say
People who have worked with me, in their own words. The rest are on LinkedIn.
“Throughout the process, Alex demonstrated vast technical acumen, visionary leadership, and a commitment to continuous learning, which ultimately resulted in one of the most diligent evaluations I've experienced in my entire career. From the start, it was clear that Alex is proactive when attacking challenges, finding solutions, and setting his teams up for success.”
Nick MilanoData + AI at Databricks · Alex was Nick's client at Kin
“I tapped Alex to be the primary architect for the technical underpinnings of a transformational data infrastructure project. Alex was never afraid to dive into incredibly big, complex and technically challenging projects... He was a strong evangelist, educating and communicating with stakeholders across the company.”
Michael KowalchikManaged Alex at Pluralsight
Talks and writing
Selected conference talks on the systems behind this work.
High-consequence work only. If the problem is not worth senior time, I will say so quickly.
A good fit
The problem is consequential and bounded, not a search for a new hire.
Senior judgment will actually change the outcome, not just add a signature.
Your team needs an independent perspective before committing to a direction.
The architecture decision has consequences you will live with for years.
You need clarity before a migration, a platform bet, or a vendor decision.
You are a consultancy or partner that needs specialist depth for one part of a larger engagement.
Not a fit
You need permanent engineering management.
You are trying to fill a fractional executive role.
You need another engineer for six months. That is staff augmentation.
The work is long-term ticket execution.
You need basic BI or dashboard development.
You need generic dbt implementation.
You are trying to build an entire outsourced engineering function.
A normal permanent hire is obviously the better answer here, and you know it.
How we start
01 A 30-minute call to see if the problem is the kind I take.
02 If yes: a scoped proposal with outcomes, timeline, fees, and how we work.
03 Then access, context, and the people who own the platform in production.
Confidentiality
Client work stays quiet unless you ask to be named. Prior employers show up here only where the outcomes are already public.
Availability & fees
Taking a small number of engagements
Terrain Survey and Route Plan are fixed fee. Traverse is a bounded, capped engagement tied to the transition, not an open-ended retainer. Exact numbers after we talk.
Working with US and EU clients
Remote across the US and Europe, on overlapping US and EU hours. Engagements run in English.
I have designed, shipped, and operated multi-region platforms under real data-residency requirements, including EU and India.
Two-week notice on scheduling changes, and a written summary after every working session.
Terms
Mutual NDA before any non-public technical detail. Yours or mine.
Client work stays confidential. Nothing appears on this site without written permission.
Fixed fee or a capped, bounded scope, agreed in writing before work starts. No open-ended time and materials, no indefinite retainer.
Advisory only. I do not provide staff augmentation or resell implementation.
08
Partners
Need a specialist inside a larger engagement?
If you are a consulting firm, implementation partner, vendor, recruiter, or investor and you own the client relationship, I can work directly with the team or behind the scenes on the portion of the engagement that needs principal-level depth: architecture, platform, streaming, ClickHouse, or technical due diligence.
You keep the relationship. I plug into a bounded piece of the work.
Useful for technical diligence ahead of an investment or acquisition.
Useful when your team needs a second, senior technical opinion mid-engagement.
No partner program, no referral fee structure. Just a scoped engagement.
09
Contact
Tell me what is on fire.
Or almost on fire. Thirty minutes on the calendar, or a short note. Problem, constraint, timeline is plenty. I reply within 48 hours. If it is not a fit, I will say so... saves us both a longer meeting.
