Senior engineers who join your team, not a vendor queue.
Data, analytics, ML, and platform engineers based in Latin America, working overlapping US business hours. They attend your standups, commit to your repository, and take their turn on call.
Profiles we place
All senior. We do not place juniors under supervision and bill them as senior — that model is why staff augmentation has the reputation it has.
Data Engineer
Builds and operates the pipelines: ingestion, orchestration, and the reliability work that keeps them running. Comfortable being paged.
Python · SQL · Spark · Airflow / Dagster · Snowflake / Databricks · Terraform
Analytics Engineer
Owns the transformation layer and the definitions the business argues about. Translates between what a stakeholder asked for and what the data can support.
dbt · SQL · Looker / Power BI / Tableau · Git · data modeling
ML Engineer
Takes models from a notebook to production and keeps them there: serving, monitoring, retraining, and the evaluation work that comes before any of it.
Python · PyTorch / scikit-learn · MLflow · feature stores · LLM APIs and evaluation
Platform Engineer
Owns the infrastructure the data team runs on: environments, CI/CD, cost control, access, and the boring reliability work that makes everything else possible.
Terraform · Kubernetes · AWS / GCP / Azure · CI/CD · observability
How the engagement works
Monthly, per engineer, no placement fee. Cancel with 30 days' notice.
- You interview, you decide
- We send two or three candidates per role, already screened technically. You run your own interview loop and reject anyone for any reason. We do not charge for the search.
- They work as part of your team
- Your repository, your standups, your ticket tracker, your on-call rotation. We do not insert a project manager between you and the engineer, and we do not run a parallel process you have to sync with.
- Monthly rate, 30 days' notice
- A single monthly rate per engineer, quoted before you interview. No placement fee, no conversion fee if you later hire them directly, no minimum term beyond the notice period.
- We stay technically accountable
- If an engineer is not working out, that is our problem to fix, and we replace at our cost. Screening is done by engineers who have shipped the same work, which is why this happens rarely.
Onboarding timeline
- Week 0 — intake call: role, stack, and what "senior" means for this team
- Week 1 — two to three screened candidates presented
- Week 2 — your interview loop
- Week 2–3 — contracts, access, and environment setup
- Week 3 — first commits
An honest comparison
Staff augmentation is not always the right answer. Here is where each option actually wins.
| Criterion | Hiring in-house (US) | Integral Data augmentation | Offshore agency (distant time zone) |
|---|---|---|---|
| Time to first commit | 2–4 months including notice periods | 2–3 weeks | 3–6 weeks |
| Cost per senior engineer | Highest, plus benefits and recruiting fees | Lower than US in-house | Lowest |
| Time zone overlap | Full | 4–8 hours, US business hours | 0–3 hours, handoff model |
| Who screens technically | Your team, at your cost in hours | Practitioners, before you see the candidate | Often a recruiter against a keyword list |
| Knowledge retention | Stays when they stay | Stays while engaged; documentation is on us | Usually leaves with the contract |
| Best when | The role is permanent and core to your product | You need senior capacity now, or capacity that flexes | Cost is the dominant constraint and the work is well specified |
If your role is permanent, central to your product, and you can wait a quarter, hire in-house. We will tell you that on the call rather than after you have signed.
Tell us about the role
Send the stack, the seniority you need, and what the first ninety days would look like. If we do not have the right person, we will say so instead of sending an approximation.
Discuss a role