Humble, hungry, and smart. You enjoy building the foundations that analytics, machine learning, and AI applications depend on. You get energy from solving difficult engineering problems and making complex systems simpler, more reliable, and easier to operate.

You break large data challenges into manageable pieces. You care about architecture, performance, quality, and maintainability. You use AI tools as part of your workflow, but you validate everything through engineering rigor and testing.

You can discuss platform design with architects, troubleshoot production issues with engineers, and explain technical trade-offs to business stakeholders. We can put you in front of a client on week one.

The role

You build enterprise-scale data platforms that power analytics, machine learning, GenAI, and business-critical reporting.

You own major components end to end: ingestion pipelines, transformation frameworks, data models, orchestration, monitoring, and platform integrations. You work as part of a small pod alongside Data Scientists, Full Stack Engineers, Architects, and client stakeholders.

This is data engineering first. Reliability, scalability, governance, and operational excellence matter as much as shipping features.

You work directly with clients, not behind a ticket queue.

What a real week looks like

Build a new ingestion framework that processes customer data from multiple enterprise systems into BigQuery.

Debug a production incident where a Dataflow job succeeds in development but fails in a client's restricted GCP environment.

Design a Lakehouse architecture using BigQuery, Dataproc, and Databricks and explain the trade-offs to client stakeholders.





Partner with a Data Scientist to productionize a Vertex AI model and automate feature pipelines.

Review pipeline performance and investigate why costs increased after a schema change.

Mentor a Grade 8 engineer who is struggling with Spark optimization and pipeline design.

Use AI development tools throughout and catch the places where the AI is confidently wrong.

What you'll do

Design and build scalable batch and real-time data platforms.

Own the architecture of your component: ingestion patterns, orchestration, lineage, monitoring, security, governance, and resilience.

Develop enterprise-grade ETL and ELT pipelines across cloud and hybrid landscapes.

Integrate enterprise systems such as ERPs, CRMs, identity platforms, APIs, streaming systems, and legacy applications.

Build data products that support analytics, machine learning, GenAI, RAG, and agentic AI applications.

Design and maintain data lakes, warehouses, and Lakehouse platforms.

Run production data workloads with monitoring, observability, cost optimization, and operational controls.

Take proof-of-concepts into production with testing, deployment pipelines, data quality validation, and governance controls.

Present architecture decisions to client teams and explain trade-offs in plain language.

Review code, mentor Grade 8 engineers,



and raise the engineering quality bar across the pod.

Strong Python and SQL.

ETL and ELT architecture, data modeling, pipeline orchestration, and distributed processing.

Data quality, lineage, reliability engineering, and observability.

You can independently design, build, and operate production-grade data platforms.

Strong hands‑on experience with:

- BigQuery
- Dataflow
- Pub/Sub
- Cloud Composer (Airflow)
- Vertex AI integrations
- Cloud Functions
- IAM and security services

You understand cloud‑native design patterns and production operations on GCP.

DataOps

Infrastructure as Code.

Automated testing.

Release management.

Monitoring, logging, and alerting.

You build systems that teams can operate reliably at scale.

REST APIs, messaging systems, event‑driven architectures, and enterprise applications.

Kafka, Pub/Sub, Event Hub, or similar technologies.

Relational and NoSQL databases.

You have integrated with real enterprise environments and understand operational constraints.

AI and ML platform enablement

Data foundations for Vertex AI and machine learning workloads.

Model‑serving integrations.

Support for GenAI, RAG, vector search, and agentic AI systems.

You understand how data platforms enable AI applications.

Copilot, Cursor, Claude Code, Gemini Code Assist, or similar tools as part of your daily workflow.

A deliberate approach to AI‑assisted development, testing, debugging, and documentation.

You teach effective AI‑assisted engineering practices to others.

Communication

Clear technical writing and documentation.

Comfortable discussing technical trade‑offs with client teams and executives.

📌 Data Engineer (México)
🏢 Entellux
📍 México