For pipelines that keep working after the schema changes
Hire Data Engineers
A pipeline that works perfectly on the day it ships can still silently break the moment an upstream schema changes or a volume assumption stops holding. These engineers are tested on catching that exact fragility. They are then placed to keep your data trustworthy as everything around it keeps changing.
- Profiles typically within two business days
- Replacement Assurance: If an engineer is not the right fit, a replacement from our own team starts within ten business days of your request, at no extra cost
- You interview before placement if you want to
- At least four hours a day inside your core working hours. Two hours plus a daily written handover for the US West Coast
- 14-Day Risk-Free Trial: For your first engineer on a time-and-materials engagement. Up to 14 calendar days or 80 logged working hours, whichever comes first. End within the trial and pay nothing for trial hours. Continue beyond it and those hours are billed at the agreed rate. Unpaid trial work remains ours if you stop
How we evaluate a data engineer
The tasks, the signals and the onboarding, walked through by the people who run them.
90 secSample frame · video pending01Published, not promised
The assessment behind every engineer we send.
The data engineering bar is published, not assumed: five competencies, the exact task behind each, and the shortcut that ends a candidacy. Most interviews ask about tool preferences. This one asks whether the pipeline survives the next schema change.
What we listen forWhether they design for change, not just for today's schema.
An upstream team changes a field's data type without warning, and a downstream pipeline silently starts producing wrong numbers. Diagnose and propose a fix.
- What strong sounds like
- Proposes schema validation at the pipeline boundary that would have caught the change loudly instead of silently.
- What gives us pause
- Fixes the immediate wrong numbers without proposing anything that would catch the next silent upstream change.
Python · SQL · Spark
Airflow · dbt · Kafka
Snowflake · BigQuery · Redshift
Great Expectations · Terraform · Docker
02Who you actually meet
The bench, priced by seniority.
Three seniority tiers, priced honestly, and the engineers who fill each one today. Real names and photos take their place as soon as we can share them, and the standard stays exactly the same.
Engineer
Features scoped tightly enough to ship inside your review bar, every time.
The right first hire when your seniors already set the standard.
$2,600–3,300per month
Senior
The hard module nobody else wants to own, taken end to end.
Ownership of a surface, without adding a manager to your headcount.
$3,600–4,600per month
Lead
The workstream's decisions, made daily, so nothing waits on a meeting.
One accountable voice for a pod or initiative that can't afford to drift.
$4,900–6,100per month
Data engineer
3 yrs · US East overlap
Built the reconciliation check that now catches a revenue discrepancy before finance ever sees it
- Python
- SQL
- dbt
Senior data engineer
7 yrs · UK + EU hours
Made a pipeline safely rerunnable from any failure point, ending an afternoon-long manual ritual
- Spark
- Airflow
- Kafka
Lead data engineer
10 yrs · EU + US split
Owns the warehouse cost model that found the exact query pattern driving runaway spend
- Architecture
- Snowflake
- Terraform
- Great Expectations
Indicative ranges, representative profiles. Exact figures and named engineers arrive with your shortlist.
Weighing this against a freelancer, a direct hire or a marketplace? See the honest comparison
03From stated problem to first change
Four steps, each closing on evidence.
This sequence tests judgement about fragility, not familiarity with a specific warehouse. Stopping at any point along the way owes us nothing.
Define the pipeline problem
We map where your pipelines' quality or resilience is actually fragile. The brief names that gap, not a list of data tools to know.
A brief your leads signMatch against your pipelines
Candidates are screened against your actual data volume, your warehouse choice and your review culture. Three engineers who understand your pipelines beat thirty who list SQL on a CV.
Three fits, not thirty CVsAssess on the real tasks
Your team can watch the schema-change and data-quality exercises directly. What shows up is fragility judgement, not familiarity with a specific orchestration tool.
Evaluation you can watchOnboard inside your process
Access, standards and a first bounded improvement to a real pipeline. Continuity cover keeps a departure from ever becoming a data outage you have to explain.
A merged change in week one
04Evidence and proof
Engineers who kept the data trustworthy as things changed.
Data engineering proof is a pipeline that survives real schema change, not a diagram that looked right on day one. Every record here waits for written client approval before production.
Explore case studies
A revenue discrepancy caught by a pipeline, not by finance.
Tern's dashboard once showed revenue figures that finance had to catch manually against their own numbers. An embedded data engineer built automated reconciliation checks that now catch a discrepancy before anyone downstream sees it.
- Manually caught discrepancies
- Zero, going forward
- Time to build reconciliation
- One sprint
He made our pipeline safe to rerun from any failure point, which used to take him an afternoon to untangle manually.
Sample · pending client approval
She found the exact query pattern that was driving our warehouse costs, not just a generic optimisation.
Sample · pending client approval
05Three shapes, one standard
Add an engineer, a data pod, or a practice.
Most teams start with one engineer strengthening existing pipelines. Some need a pod that owns data quality and cost end to end. Where a whole data platform delivered matters more than one seat filled, we point you to the practice instead.
- Team extension
One named engineer
A single data specialist inside your direction and your reviews. The smallest commitment, and the natural first step.
- Delivery pods
A data pod
A pod with its own lead that owns pipeline quality, cost and warehouse design end to end. You set priorities while the pod carries delivery.
- Data & Cloud
The practice, not the person
Where a whole data platform delivered matters more than one seat filled, data and cloud engineering takes that scope on instead.
06Straight answers
Asked before a data engineering hire is signed.
01What does a data engineer actually do day to day?
Designs schema validation and data-quality checks, chooses batch or streaming architecture matched to the real requirement, and keeps warehouse cost proportional to actual query patterns. The unglamorous majority of the work is catching fragility before it becomes an incident.
02Do you hire for a specific data warehouse?
Matched to your actual stack. Schema and cost judgement transfers across Snowflake, BigQuery and Redshift, then the evaluation confirms familiarity with whichever you run.
03Can we run our own interviews as well?
Yes, the published tasks are built to be reused directly. Several clients now run the data-quality exercise themselves in later hiring rounds.
04How fast can a data engineer start?
A shortlist arrives within days from a bench evaluated continuously. Real start speed after that tracks how quickly your side can grant pipeline and warehouse access.
05Who manages the engineer day to day?
Your leads set priorities and hold the quality standards. We carry what sits behind that instead: continuity, payroll, replacement risk and the handoff work when plans change.
06What if the engineer is not the right fit?
Say so within the first weeks and the replacement is on us. The next engineer is briefed on the exact gap, which keeps a second miss uncommon.
07Do your data engineers use AI coding tools themselves?
Yes, to scaffold pipeline code and validation tests. The pipelines they build for you are governed separately, through the schema and quality checks described above.
08How does pricing work for a hired data engineer?
The bands above frame the honest range for this specialism; the shortlist fixes the precise figure once scope is agreed. Monthly billing, no placement fee, notice-period exit only.