About the role
The Data Engineer role consists of building the pipelines and warehouses that turn scattered operational data into something analytics and AI can actually use. Unglamorous, foundational, and the hidden reason most AI initiatives underdeliver: models are only as good as the data reaching them. If your dashboards are stale and your AI pilot 'doesn't work,' the root cause usually lives here.
Monthly rate
$5,000–$7,500/mo
All-in: contract, benefits, equipment, IP
Experience
10+ years typical
Location
Latin America
Argentina · Colombia · Mexico
Timezone
Full US overlap
Fluent English, onboarded in one week
Core stack
AI tools, daily
Verticals seen
What they own — and what they don't
What they own
- Build and run data pipelines: ingestion, transformation, orchestration — batch and streaming
- Design the warehouse or lakehouse: modeling, partitioning, cost-aware storage
- Own data quality: freshness, schema contracts, alerting when upstream breaks things silently
- Feed downstream consumers reliably — BI, ML training, RAG indexes, product features
- Keep the data platform bill sane as volume grows
What they don't — and who does instead
- Analyze the data for business insight — that's a Data Scientist
- Train models on it — that's an ML Engineer
- Build the LLM features on top — that's an AI Engineer
- Administer production app databases — that's platform/DevOps territory
Who hires this role, and for what
Companies whose reports disagree with each other. Three dashboards, three revenue numbers. A Data Engineer builds the single source of truth everyone reconciles against.
Teams about to invest in AI. RAG, fine-tuning, and analytics all consume clean, fresh, well-modeled data. Smart CTOs fix the plumbing before hiring the AI team.
Scale-ups outgrowing their startup stack. The cron-jobs-and-CSVs era ends around Series B. What replaces it is a real pipeline layer someone senior designs.
- 01
Warehouse / lakehouse build. From scattered sources to one modeled, documented, queryable platform — Snowflake, BigQuery, Databricks.
- 02
Streaming pipelines. Events flowing in real time for product features, alerting, or live analytics — Kafka and friends.
- 03
AI data readiness. The corpus, freshness, and access controls that RAG and training pipelines depend on.
- 04
Pipeline rescue. Replacing the tangle of scripts nobody dares touch with orchestrated, observable, testable flows.
Work our engineers at this role have shipped
- Real-time event pipeline (Kafka + Flink + Snowflake) for a fintech risk system
- dbt-based warehouse rebuild replacing a tangled Redshift setup — 3× cheaper, 5× faster reads
- Streaming ingestion + feature engineering pipeline feeding a live recommendation model
Do you actually need a Data Engineer?
You do, if:
- Analysts and models consume data that's stale, inconsistent, or hand-exported
- One person's undocumented scripts are load-bearing for company reporting
- An AI initiative is planned and the source data lives in six systems
- Warehouse costs grow faster than data volume
You probably don't, if:
- Your data fits in one Postgres and one BI tool — you may just need good habits, not a hire
- The gap is insight, not infrastructure — that's a Data Scientist
- You need one integration, once — scope it as a project instead
Not sure which role fits? Tell us the problem instead of the title — we'll tell you what we'd actually staff, even if it's not this. If it is this: discovery call today, matched profiles in 48 hours, onboarded in a week.
Hire a Senior Data Engineer