Hire Data Engineers
Hire top AI-augmented data engineers in 2-4 weeks.
Engineers who build data platforms with the tests that catch a broken feed before a dashboard does, and the lineage to explain where a number came from.
Hire Data Engineers
Trusted partner for startups and enterprise engineering teams.
Skip the recruitment bottlenecks.
Hire our full-time engineers and onboard them within weeks. Choose from three engagement models: staff augmentation, dedicated teams, or software outsourcing.
Andrés P.
React Technical Lead
11 years experience
Priya N.
Front-End Developer
9 years experience
Marco L.
Senior Backend Developer
8 years experience
How our vetting process works.
Everyone who reaches your shortlist has been through the same three stages, and the people running them write code for a living.
Applicants sit a written exercise and then talk to an engineer who has done the job. What comes out of it is a view on how somebody thinks, which is the part a CV cannot tell you.
We evaluate technical ability across languages, testing and the AI coding tools we expect them to use as a baseline - and the interpersonal skills that make somebody worth working alongside.
Our engineers work in English every day, so we check it properly. We also check where they have worked and how they left, and we speak to somebody who was not on the reference list.
Because the screening happens before you see anyone, the shortlist is short and every name on it is worth your time. From there we match the right people to your stack, your time zone and the way your team runs.
Benefits of working with us
Engineers who join your team rather than a queue of tickets, working in the technologies you already run. What that gets you:
Engineers, not a body shop
The people you meet are the people who join. Nobody is swapped out after signature.
They arrive knowing the tools
Our engineers work with AI assistants every day, and are expected to review what those tools produce rather than forward it.
Your process, not ours
They join your stand-ups, your board and your review culture. We do not impose a methodology on a team that already has one.
Overlapping hours
We staff to your working day, so a question asked in the morning is answered before you close the laptop.
Scale in either direction
One engineer or eight, and that number can change as the roadmap does without renegotiating everything.
The work stays yours
Code, documentation and access are yours from the first commit. Nothing is held back as leverage.
Your tech stack. We have it covered.
- .NET
- AWS
- C#
- Django
- Java
- JavaScript
- Microsoft Azure
- React
- Ruby
- TypeScript
- Xamarin
- Laravel
- Tailwind CSS
- Shopify
- WooCommerce
- WordPress
- Squarespace
- C
- Rust
- Elixir
- Dart
- Swift
- Progressive Web App
- Spring Boot
- Symfony
- Grails
- iOS
- Flutter
- Ionic
- PostgreSQL
- Oracle
- MongoDB
- Docker
- Red Hat OpenShift
- Selenium
- LLM
- Ollama
- Claude Code
- Github Copilot
- Windsurf
- ServiceNow
- Mulesoft
- GIS
- IoT
- AI
- Angular
- C++
- Go
- Google Cloud
- Kotlin
- Node.js
- PHP
- Python
- Salesforce
- Vue.js
- Next JS
- CodeIgniter
- DevOps
- Magento
- BigCommerce
- Webflow
- Wix
- HubSpot CMS
- Perl
- Objective-C
- HTML5
- GraphQL
- API
- ASP.NET
- Express
- Ruby on Rails
- Android
- React Native
- Unity
- MySQL
- SQL Server
- Firebase
- Kubernetes
- Chaos Monkey
- Generative AI
- Gemini
- Codex
- Copilot
- Cursor
- Replit
- SAP
- SharePoint
- SaaS
- Drupal
Our process. Simple, seamless, streamlined.
We have reps
across the region.
Speak with a client engagement specialist near you.
- Tell us what you are building.
One call about the work, the stack and the deadline. We tell you what shape of team fits it - including when the answer is fewer people than you asked for.
- Meet the shortlist and pick.
You interview the data engineers we put forward, on your own terms. Nobody joins your team without you having said yes to them.
- They start, and we keep watching.
We stay close through the first months and check the work is landing. If it is not, that is ours to fix rather than yours to manage.
Case study
A WordPress site weighed down by eleven plugins was costing Northwind organic traffic. We rebuilt it on Next.js in five weeks. Read the entire Northwind case study.
An honest comparison
Hiring freelance data engineers
versus hiring through us.
Freelancers are the right answer for some work, and we will say so. Here is where each way holds up, and where it does not.
A freelance marketplace
Fast to start and cheap by the hour, as long as the work is small and well defined.
- Someone can start this week
- You pay only for the hours you use
- Good for a bounded, one-off piece of work
- You do the screening, and you carry the mistakes
- No cover when they take a holiday or disappear
- Code review, security and compliance are yours alone
- Rates rise once they know you depend on them
Hiring data engineers through us
Built for work that outlasts a single ticket, where the team still has to make sense in a year.
- Vetted by engineers before you see anyone
- A replacement, at our cost, if the fit is wrong
- One contract covering payroll, compliance and equipment
- The same person for the length of the engagement
- Your code, your repository, your IP from day one
- Two to four weeks to start, not two days
- Not the cheapest hourly rate you will find
A comprehensive guide
How to hire the best data engineers in 2026
Most hiring goes wrong before anyone is interviewed - in the description of the job. What follows is the sequence we use, written so you can run it yourself, and the questions worth asking at each step.
What to look for when hiring data engineers.
Whether you are hiring a freelancer, a full-time employee, an engineer to sit inside your own team, or a supplier to run the whole build, these are the points worth keeping in mind while you evaluate.
1. Decide what the person is actually for
Write down the first three things they will finish. If you cannot, the role is not ready to hire for, and the interviews will end up being about personality instead of work.
2. Choose the seniority before the stack
A senior engineer in an unfamiliar framework will usually beat a junior in a familiar one. Screening on the stack first is how teams end up with people who can only do what has already been done.
3. Write a description someone good would answer
Name the product, the problem and the size of the team. Strong candidates are choosing between offers, and vagueness reads as a company that does not know what it wants.
4. Make the exercise resemble the job
Give them a small piece of real work and an hour, and let them use the tools they would use on the job, AI assistants included. What you learn is how they judge output, which is the skill that now matters.
5. Interview for how they disagree
Ask about a decision they argued against and lost. You are hiring someone who will have to tell you when you are wrong; find out now whether they can.
6. Check the references you were not given
The named referee is always positive. Ask who they reported to and who reported to them, and speak to one of each.
7. Plan the first month before they sign
Access, a buddy and a shippable first task. Good engineers leave early far more often because nothing was ready than because the work was hard.
What to avoid when hiring data engineers.
Most of the damage is done after the offer is signed, not before it. These are the four that cost teams the most, and all four are avoidable.
1. A job description that describes a different job
Hiring somebody for one thing and giving them another is the fastest way to lose them, and they will tell people why they left. If a good candidate does not quite fit the description, say so and ask - engineers rarely want to change discipline for a job, but a genuinely interesting problem is a different conversation.
2. Treating the salary as the whole offer
Rates for the same role vary enormously, and the highest bid does not buy the best engineer any more than the lowest one buys a bargain. Decide what the hire is worth to you before you hear a number.
What keeps good engineers is rarely the money on its own: the work, the people they answer to, whether their opinion changes anything. Those are the parts you can actually compete on.
3. Onboarding that starts with a ticket
Dropping somebody into a sprint on day one and waiting to see what happens is not a test of their ability, it is a test of your documentation. Two days spent on the goal, the history and the parts that are held together with tape will pay for themselves in the first fortnight.
4. Leaving the expectations unsaid
If nobody has said what good looks like in this role, everyone will assume something different, and the person you hired will find out they were wrong at a review. Write down what they own, what they decide alone, and what the first three months should produce.
Ask the team what they need from the hire too. They will tell you something you had not thought of, and they are the ones who have to work with the answer.
Define what kind of engineer you want to hire.
Engineering is not one job. The differences between these disciplines decide who can do the work you have, and hiring the wrong one of them is expensive in a way that is not obvious for several months.
If you are not close to software yourself, this is the part worth reading twice. You do not need to know all of it - you need to know enough to tell which of these you are actually short of.
Front-end developers
The part of the software your customers touch: interfaces, state, layout and how fast it feels. JavaScript, TypeScript, React and CSS are the usual tools. Hire front-end developers.
Back-end developers
The logic behind the screen - data models, APIs, queues, caching and the things that decide whether the product survives its busiest day. Python, Java, C#, Go, Node.js and SQL. Hire back-end developers.
Full-stack developers
Both halves, to a working standard rather than an expert one in either. Genuinely useful on a small team, where the cost of a handover between two specialists is higher than the cost of one generalist.
Mobile developers
Applications for phones, tablets and watches, where the platform decides more than the language does. Swift, Kotlin, React Native and Flutter. Hire mobile developers.
AI engineers
The parts around a model rather than the model itself: retrieval, prompts, evaluation and the fallbacks that keep a feature honest when the model is not. Hire AI engineers.
Data engineers
The pipelines that carry data from wherever it is made to wherever it is read, and the tests that catch a broken feed before a dashboard does. Hire data engineers.
DevOps engineers
Builds, environments, deployment and monitoring - the work that turns a release from an event into a routine. Hire DevOps engineers.
QA and test engineers
Whether the software does what it was meant to. Automated coverage where it earns its keep, and somebody sitting down and using the thing where it does not. Hire QA engineers.
UX and UI designers
Not developers, and on most teams the reason the developers build the right thing. Research, flows and a design system engineers can build from without guessing.
Data scientists
Statistics and modelling rather than pipelines: finding what the data supports, and saying plainly when it supports nothing. Python, R and SQL.
Qualities of an ideal staff augmentation partner.
The questions worth asking, whoever you end up choosing. We are comfortable being measured against every one of them.
Engineers do the vetting
If a recruiter is the only person who has spoken to your candidate, nobody has assessed the work. Ask who read the code.
The person you meet is the person who joins
Ask directly whether the shortlist is who will actually be assigned. It is a common substitution and an expensive one.
IP and access in writing
Your code, your repositories, your accounts, from the first day. Anything less is leverage held over you later.
A stated replacement policy
Every partner says the fit will be right. Ask what happens, in writing, on the day it is not - and who pays for the handover.
Overlap with your working day
Four hours of shared time is the difference between a colleague and a contractor you email. Ask where the engineers actually are, not where the company is registered.
References you chose
Ask for a client who left, not only for the ones still paying. The answer, and whether you get one at all, tells you most of what you need.
Questions we are asked before signing.
How quickly can data engineers start?
Two to four weeks from the first call in most cases: a few days to shortlist, your interviews, then contracts and onboarding. A very specific stack can take longer, and we will say so at the start rather than at the end.
Do I interview them myself?
Yes, and you should. We do the screening so the shortlist is short, but nobody joins your team without you meeting them and saying yes.
What happens if it is not working out?
Tell us early. We replace the person at our cost and carry the handover. That is the point of hiring this way rather than directly.
Who owns the code?
You do, from the first commit, in your own repository. Assignment of IP is in the contract before anyone starts.
Can I hire them permanently later?
Often, yes. We would rather agree the terms for that up front than have the conversation as an argument eighteen months in.
Is this cheaper than hiring directly?
Per hour, usually not. Across a year - counting the vacancy, the agency fee, the equipment and the risk of the wrong hire - it usually is. We are happy to do that sum with you honestly.
Tell us what you need built.
We will tell you who should build it.