How many data engineers would your roadmap need this quarter, and how many can you actually hire by then? We close that gap with engineers who join your repos, standups and on-call and work your backlog like the rest of your team. You can start with one person and add more once you’ve seen the work.
People who build and run the pipelines, models and warehouses your reports and products depend on.
For teams moving data between dozens of systems, or replacing legacy ETL jobs that nobody wants to touch.
Engineers who know Snowflake beyond basic SQL, including what drives the monthly bill.
For lakehouse builds, Spark workloads and ML on the same data.
For high-volume and real-time systems where Spark, Kafka and Hadoop do the heavy lifting.
If an engineer moves on, we find the replacement and run the handover, so you don’t have to restart hiring.
92% of our engineers are senior, so the person who joins your team has already run production data platforms.
96% employee retention means the engineer you onboard in January is still on your project in December.
Five of our engineers hold Anthropic’s Claude Certified Architect credential. If your pipelines feed an AI agent or a RAG system, you can bring a certified architect into the design review.
AI-assisted development lets our engineers ship up to 50% faster with up to 20% fewer bugs, and a senior still reviews every change.
Our processes are ISO 27001-certified, and we follow GDPR and HIPAA practices for teams that work with sensitive data.
Tell us what’s blocked, which tools you run and how many people you need. We’ll get back to you within 24 hours.
You get profiles of engineers who’ve already worked with your platforms and the kind of data you handle.
Run your own technical interview or a take-home task. The hire is your decision.
We sort out access and tooling with your team up front, so everything is ready on the engineer’s first day.
Add engineers as the backlog grows, and reduce the team once the migration lands.
You add data engineers to your existing team for as long as you need them, whether that’s one migration or a longer stretch of roadmap work. They take tasks from your backlog, follow your processes and report to your lead, just like your in-house engineers. We handle the rest: hiring, contracts, payroll, equipment and replacements if someone leaves.
With augmentation, your lead owns the architecture, the priorities and the result. If you’d rather hand over a whole project with a defined outcome, data engineering services are a better fit, and we take responsibility for delivery end to end.
A quick way to choose is to look at who’ll own the architecture decisions. If your lead already does, augmentation is the faster path.
They work as regular members of your team. Your lead assigns tasks, they join your standups, planning and retros, and their pull requests go through the same code review as everyone else’s.
Day to day, they use your Jira, Slack and cloud accounts, follow your branch rules and naming conventions, and write documentation into your wiki. When an engineer rotates off, the context stays with you.
Most teams pair each new engineer with one in-house person for the first weeks. It speeds up onboarding a lot and gives the new engineer a clear contact for questions about the code.
If you only need one problem solved and don’t want anyone joining your team, a short data engineering consulting engagement is usually simpler.
It depends mostly on how well your platform is documented and how quickly access is granted. On a clean setup, an experienced engineer ships useful changes soon after access is in place. Messier setups take longer, because tribal knowledge has to be written down first.
What usually slows things down:
You can shorten this yourself. Grant access before day one, pick two or three well-scoped first tickets, and name one person on your side who answers questions during the first two weeks. We sort out tooling and access with your team up front, so the engineer isn’t stuck waiting on permissions.
You own everything the engineer produces: code, data models, documentation and configs. The IP terms are agreed in the contract before kickoff.
Engineers work in your accounts and repositories with the permissions you grant. You decide what they can see. Where it’s enough for the task, you can limit access to production data and give them masked or test datasets instead. When the engagement ends, you revoke access the same way you would for any employee.
We sign an NDA before you share any details. Our processes are ISO 27001-certified, and we follow GDPR and HIPAA practices for teams that handle personal or health data.
Start from the work that’s blocked:
If your list mixes several of these, we’ll help you split it into roles on the first call.
Yes, and many teams do. Start with one engineer on the most urgent piece of work and see how they fit with your team and your code. Once you’re confident, add more people, and we’ll match them to the same stack and conventions.
Scaling down works the same way, with notice agreed in the contract. When a migration lands, you can reduce the team. If the backlog grows again, we can add engineers back.
If someone isn’t the right fit, tell us. We’ll find a replacement and run the handover.
Share your stack and backlog, and we’ll send CVs of engineers who’ve worked with both.
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Outsourcing
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