Screened for how they keep data correct, fresh, and on time.
Why STACK IT
Most agencies optimize for volume. We optimize for the one hire who’s right, vetted by people who understand the work.
We screen for how a candidate models data, builds pipelines, and reasons about scale and failure, not how many tools they can list. No one reaches you without two of our recruiters signing off.
Fake profiles, proxy interviews, and AI-assisted answers are everywhere in tech hiring. We meet each candidate face-to-face on video and screen for AI patterns, so who you interview is who shows up.
Data platforms compound, and the person who built the pipelines knows how they fit together. We align trajectory, growth, and total comp so your hire stays with the platform they build.
Success-based and non-exclusive, no upfront fees, no retainers. We invoice on your hire’s first day, not before.
The payoff
Hiring well costs less than you think, and a strong hire changes far more than the work in front of them.
One source of truth everyone trusts.
Modeled, tested data means the numbers line up.
Pipelines fail loudly, not silently.
Monitoring and tests catch bad data before it reaches a report.
Self-serve data the whole team can use.
Clean models and documentation turn requests into queries.
Fresh data when decisions are made.
Reliable scheduling and streaming keep data current.
It scales with the data, not against it.
Partitioning, warehousing, and right-sized compute keep it fast.
How we screen
We screen for how data engineers actually think. Every shortlist is judged against the same five criteria that predict whether someone delivers in your codebase.
Designs schemas and transformations that stay correct and performant as data and questions grow, with SQL that holds up under real scale.
Builds ingestion and transformation (batch or streaming) that is idempotent, testable, and recoverable, not a pile of cron jobs.
Real command of a modern warehouse or lakehouse (Snowflake, BigQuery, Databricks, or similar) and how to keep it fast and affordable.
Tests, lineage, and monitoring that catch bad data before it reaches a dashboard, with calm root-cause work when something slips.
Translates between analysts, scientists, and stakeholders, and documents models so the data is usable without a Slack message.
Proof it works
Discover what changed once the right hire joined our clients’ team.
A SaaS analytics team was making decisions on data up to four hours stale, fed by a brittle nightly batch. The engineer we placed re-built ingestion as tested incremental models, taking data latency from four hours to minutes.
Rebuilt a fintech reporting pipeline with tests and lineage, so the month-end close reconciled cleanly with no manual fixes.
Re-modeled and partitioned a healthcare data warehouse, taking heavy analytical queries from minutes to seconds.
Real technical screening, a calibrated shortlist in days, and candidates vetted for fit, not just resumes. Let’s start your search.
Specializations
Whatever your team runs on, we screen for the people who do the work right.
Ingestion and transformation that is tested, recoverable, and repeatable.
Modern warehouses and lakehouses kept fast and affordable.
Event and streaming pipelines for data that can’t wait for a batch.
Reliable scheduling and dependencies across complex data workflows.
Tests, contracts, and monitoring that keep bad data out of production.
Catalogs, lineage, and access controls that make data trustworthy.
The cost of waiting
An empty seat doesn’t delay work, it redistributes it. The longer the search drags, the more it costs.
Every week a role stays open, the cost lands on the team you already have.
Speed isn’t a nice-to-have. It’s the difference between a gap and a setback.
Time to fill this role
How you hire
Two models, one standard of quality. Bring on the data engineers you need the way that fits your timeline and budget.
Permanent
Best when you’re building the team for the long term.
Contract
Best when you need delivery capacity now, without adding headcount.
Not sure which fits? Compare permanent vs. contract
FAQ
The questions teams usually ask before starting a search with us.
We focus on whether their pipelines survive past day one. We walk through data platforms a candidate has built and probe their modeling decisions, versioning, observability, and how they handled bad data and backfills, then have them explain the tradeoffs. Anyone can move data once, so we screen for people who keep it flowing reliably.
Ownership. A senior owns the modeling, reliability, and the decisions about how data is structured and trusted, while a junior writes jobs to a defined spec. We calibrate the shortlist to the level your platform actually needs.
Platform build-outs and migrations often fit contract, while ongoing ownership of a data platform usually calls for a permanent hire. We will steer you to the right model.
Data and AI roles are among the tightest in the market in 2026, so bands move quickly. We benchmark against recent placements rather than year-old surveys so your offer lands right.
Permanent hires are success-based: you pay only when someone starts, with no upfront fee, backed by our 90-day guarantee. Contract runs on a transparent hourly rate. We will walk you through the specifics on an intro call.
Still have a question? Talk to a recruiter
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Every search keeps your hiring audit-ready in Ontario.
Start a search
Share the role and we’ll reply within one business day with a calibrated shortlist of three to five data engineers, screened for your stack and your team.
