Here is something that will not surprise you if you have been in data engineering for more than a couple of years: the roles you would actually want are rarely the ones you find on a job board.
They get filled quietly. A consulting firm wins a Databricks migration and needs two architects in three weeks. A Snowflake account team flags a customer who is about to start an implementation with nobody to run it. In both cases the hiring manager does not open a requisition and wait. They ask who is already known.
If you are not in that conversation, the strength of your resume is irrelevant. You were never in the room.
The best data roles are not competitive. They are filled from a shortlist you were either on or you weren’t.
The migrations are happening now, and they are staffed quietly
This is not a hypothetical hiring market. Of the enterprises already running Apache Iceberg in production, 79% are migrating or plan to migrate their remaining data assets within a year — meaning most of them are mid-project right now. On the platform side, Databricks reports more than 20,000 organisations and over 60% of the Fortune 500 on the platform.
Every one of those programmes needs people, and the delivery firms running them are perpetually short. We see it from the inside — the roles that stay open longest on our Databricks and data warehouse desks are never the junior ones. They are the people with genuine production depth, and there are not many of you.
The problem is that you are almost all employed, quietly good at your job, and completely invisible to the firms who need you this month.
That is the problem Lakehouse was built to solve.
What Lakehouse is
Lakehouse is Sloane Staffing’s free, anonymous talent ecosystem for Data & AI professionals — Databricks, Snowflake, data engineering and data architecture.
You create a profile once. It takes about two minutes. From then on, the companies and consulting firms staffing live projects can find you directly, and a specialist who actually understands this work decides whether a role is worth putting in front of you.
That last part matters more than it sounds. The alternative — the one everybody has lived through — is a keyword parser skimming your resume for buzzwords and a recruiter who cannot tell Delta Lake from a data lake messaging you about a Java role. In Lakehouse, someone who knows the difference between an analytics engineer and a platform engineer looks at the fit before you ever hear about it. Most of what would have reached you never does, which is the point.
Anonymous in the way that actually matters
The reason most strong data engineers are not “on the market” is not that they are uninterested. It is that being visibly interested is expensive. Your manager finds out. Your name circulates. You end up in conversations you did not want in order to protect the option you did.
Lakehouse is anonymous by default, and specifically anonymous in the way that removes that risk.
Here is what a company actually sees on a listing:
| What’s shown | Example |
|---|---|
| Role and industry | Data Architect · Healthcare |
| Years of experience | 11 years |
| Skills | Databricks, Delta Lake, dbt, Spark, Azure |
| One impact story | ”Rebuilt a hospital network’s claims pipeline onto a lakehouse architecture, cutting nightly batch processing from 6 hours to 40 minutes.” |
That is the whole listing. No name, no employer, no contact details. Enough for a hiring team to want a conversation, and nowhere near enough for anyone at your current company to work out that it is you.
You stay in control of the moment it stops being anonymous, which is the only moment that ever carried any risk.
The part that pays off even if you never move
Most people who join are not actively looking. They join because being discoverable costs nothing and being invisible occasionally costs a lot — and because of the monthly market report, which is genuinely useful whether or not you ever take a call.
Every month it covers:
- Certifications gaining value — Databricks, SnowPro, Unity Catalog, and which ones companies are actually paying for rather than merely listing.
- Platforms companies are standardising on, which is the earliest signal of where the next two years of demand goes.
- Which data and AI consulting firms are expanding — the firms adding delivery capacity are the ones hiring hardest.
- Where architect and engineering rates are climbing, so you find out your market moved without having to interview to discover it.
If you want the current picture on pay before that, our 2026 salary benchmarks cover US ranges by seniority including contract rates, and our breakdown of Snowflake and the people behind it covers the skills teams hire for when they adopt it.
Who it is built for
Lakehouse is deliberately narrow. It is for people whose work sits on the modern data platform and who have actually shipped on it:
- Data engineers building and running production pipelines — Spark, dbt, Airflow, streaming.
- Data architects making the platform decisions — migrations, governance, Unity Catalog, cost.
- Analytics engineers who own the modelled layer between raw data and the business.
- Platform and ML engineers standing up the infrastructure the AI roadmap depends on.
Whether your stack is Databricks, Snowflake or both matters less than whether you have run something real on it. The consulting firms we work with are hiring for delivery, and delivery experience is the thing they cannot fake their way around.
It is not built for people at the very start of their career, and it is not built for generalists who touch data occasionally. That is not gatekeeping — it is the reason a hiring team trusts the pool enough to keep coming back to it, and the reason the roles that reach you are ones you would actually consider.
If you are somewhere adjacent — you are strong on the warehouse side but have not touched a lakehouse yet, say — join anyway. Where your skills sit relative to demand is exactly what the monthly report is for.
Not a job board, and not an open marketplace
The distinction is deliberate. Lakehouse is not somewhere you go to browse listings and fire off applications. There is nothing to scroll.
It is a curated pool of data and AI specialists that serious hiring teams come back to precisely because it is small and specific. Companies use it because the signal-to-noise ratio is high. That only stays true if it is not open to everyone with a keyboard, which is why it is built for specialists rather than for volume.
The same ecosystem runs for three other practice areas, if your work sits closer to one of those: GTM Engineering, AI Search and AEO/GEO, and Adobe Experience Cloud.
Free, two minutes, no gatekeeping
There is no paywall and no up-front vetting. We AI-check every profile for realism and internal consistency before it enters the ecosystem — that is the entire bar. No “verified” badge to earn, no ranking games, no tier you have to buy your way into. Just real people, real skills, and companies who are actually hiring.
You will get a welcome email, your profile goes live, and the monthly market report starts arriving. Then nothing happens until something worth your time does.
That is the trade. Two minutes now, in exchange for being in the room the next time a migration lands and somebody asks who is already known.