Mateo S.
Senior Data Platform Engineer
Bogota, CO - EST overlap - C1 English
- Python
- Airflow
- Snowflake
- AWS
Nearshore data engineers
Nearshore data engineers who've maintained real production pipelines
Not developers who took an online course in Airflow. Senior engineers who've owned data pipelines other teams depend on and know what breaks, why, and how to build so it does not happen again. Founded by developers, vetted by developers, working your hours.
> data_engineer.match
3 days
Matched profiles
14 days
Typical first engineer
US hours
Incident overlap
Senior
Developer-vetted
Built for data and analytics leaders who need production ownership, not another resume with Python and SQL on it.
Available in 14 days
Showing Mateo S., profile 1 of 3
Data engineers we can place
Profiles built for production data work
Senior nearshore data engineers with real pipeline ownership, cloud data platform experience, and enough operational scar tissue to know where quiet data failures hide.
The hiring risk
The problem with generic data engineering hires
Most nearshore vendors staff a data engineer the way they would staff any backend role: find someone who has used Python and SQL, then plug them in. That works until a pipeline silently fails on Friday, a schema change breaks three downstream dashboards, or the hire turns out to have written analysis scripts without ever maintaining production infrastructure.
Data engineering mistakes stay quiet until they become expensive: a stale dashboard an executive uses to make a decision, or a broken feed that lets a model degrade unnoticed. You need engineers who have owned that responsibility before, not someone encountering it for the first time in your environment.
How we actually vet
Production ownership, made concrete
We're founded by developers, not recruiters. Our screens test what matters when a data system is live and other teams rely on it, not how many tools appear on a resume.
Real pipelines, not one-off scripts
Candidates walk us through a pipeline architecture they have owned: what depended on it, how it was monitored, what happened when it failed, and what they changed afterward. Writing a working script once is not the same as maintaining infrastructure other teams rely on.
Judgment under failure
We probe for the unglamorous production work: backfills, schema drift, late-arriving data, duplicate events, partial loads, and silent failures. That is where tool familiarity and real operational ownership separate quickly.
Engineers vetting engineers
The technical screen is run by developers who know how production systems behave. We ask follow-up questions about tradeoffs, observability, recovery, and downstream impact instead of checking keywords against a job description.
Nearshore vs offshore
Broken pipelines don't wait for business hours
Data infrastructure fails in ways that are urgent precisely because they are quiet. A feed dropping records overnight, a schema change breaking a job, or an executive dashboard showing the wrong numbers needs attention the same day, often the same hour.
Offshore data teams (no overlap)
A broken feed is found after the handoff. A schema question waits until tomorrow. Analytics, product, and finance lose a business day while the clarification loop crosses time zones.
Nearshore data engineers (US overlap)
Your engineer is already online when a stakeholder needs to define a metric, confirm a schema change, or debug a failed pipeline. Decisions and recovery happen in the same workday.
For data engineering, time-zone overlap is part of system reliability, not a scheduling perk.
Stack we staff for
Data engineers matched to your production environment
We match for the systems your engineer will own, whether you need a nearshore ETL developer, analytics engineer, streaming specialist, or senior data platform engineer.
Languages
- > Python
- > SQL
- > Scala
Orchestration
- > Airflow
- > Dagster
- > Prefect
Processing
- > Spark
- > dbt
- > Kafka
Warehousing
- > Snowflake
- > BigQuery
- > Redshift
Infrastructure
- > AWS & GCP data services
- > Terraform
- > Data pipeline CI/CD
Not just a placement
One system for vetting, hiring, payroll, and support
Most nearshore vendors hand you a resume and disappear. We built the full operating layer we wished existed when we were engineers looking for strong teams to join.
Vetting
Technical screens run by developers who understand production data infrastructure, not a keyword match against a job description.
Hiring
Matched candidates in days, selected for your stack, seniority, availability, and the systems they will actually own.
Payroll & compliance
One contract, one invoice, and no separate EOR to set up before your engineer can start contributing.
Ongoing support
Check-ins and a real point of contact, so a distributed data hire is never something you are managing alone.
New to the engagement model? See Nearshore Developers & EOR, Explained.
Building an AI or ML product on top of this infrastructure? See Hire Nearshore AI Engineers for the engineers who build the models and applications on top.
Frequently asked questions
Common questions about hiring nearshore data engineers
Why nearshore instead of offshore for data engineering?+
Pipeline failures and schema questions are time-sensitive. A broken feed or unclear business-logic definition cannot wait a full day for a reply. Nearshore engineers overlap your working hours, so problems can be caught, discussed, and fixed the same day.
How is a data engineer different from an AI or ML engineer?+
Data engineers build and maintain the pipelines and infrastructure that move, transform, and prepare data for BI, reporting, products, and AI systems. AI and ML engineers build models and applications on top of that data. The roles are adjacent, but the production skills and ownership are different.
Can you staff our specific warehouse, orchestrator, or processing framework?+
Yes. Tell us the stack, architecture, and ownership boundaries on the discovery call. We match against those requirements before you see a profile, whether you need ETL developers, analytics engineers, or senior data platform owners.
Is payroll and compliance handled, or do we need our own EOR?+
Handled. One contract covers the engagement from vetting through ongoing payroll, so you do not need to establish a local entity or coordinate a separate EOR.
How fast can a data engineer be working in our pipelines?+
Matched, developer-vetted profiles are typically ready within 3 days. A first engineer can often start in about 14 days, depending on role specificity, your interview process, and onboarding requirements.
Nearshore model
New to nearshore hiring?
Nearshore Developers & EOR, Explained. The guide explains nearshore development, EOR, payroll, compliance, and ongoing support before you choose a specific role, country, or industry page.
Related hiring paths
Build the right nearshore team around this role
Ready to hire
Tell us what your data infrastructure looks like. We'll show you matched, developer-vetted engineer profiles within 3 days.
Share your pipelines, warehouse, orchestrator, processing stack, seniority needs, and timeline. We will come back with a realistic view of who is available and how quickly we can move.