Data Engineer, AI & Data Platforms

Contractor · Remote in Mexico · Full-time · Long-term · CST time zone

We're looking for a humble, hungry, and smart Data Engineer to build the enterprise-scale data platforms that power analytics, machine learning, GenAI, and business-critical reporting for leading global clients. You'll own major components end to end (ingestion pipelines, transformation frameworks, data models, orchestration, monitoring, and platform integrations) 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'll work directly with clients, not behind a ticket queue, and you could be in front of a client in your first week. You use AI tools as part of your daily workflow, but you validate everything through engineering rigor and testing.

Responsibilities

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 proofs of concept 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 junior and mid-level engineers, and raise the engineering quality bar across the pod.
A typical week might include:
- Building a new ingestion framework that processes customer data from multiple enterprise systems into BigQuery.
- Debugging a production incident where a Dataflow job succeeds in development but fails in a client's restricted GCP environment.
- Designing a Lakehouse architecture using BigQuery, Dataproc, and Databricks, and explaining the trade-offs to client stakeholders.
- Partnering with a Data Scientist to productionize a Vertex AI model and automate feature pipelines.
- Reviewing pipeline performance and investigating why costs increased after a schema change.
- Mentoring a junior engineer who is struggling with Spark optimization and pipeline design.
- Using AI development tools throughout and catching the places where the AI is confidently wrong.

Requirements

5+ years of experience building enterprise data platforms.
Data engineering:
- Strong Python and SQL.
- ETL and ELT architecture, data modeling, pipeline orchestration, and distributed processing.
- Data quality, lineage, reliability engineering, and observability.
- Ability to independently design, build, and operate production-grade data platforms.
Google Cloud Platform (preferred): strong hands-on experience with:
- BigQuery, Dataflow, Dataproc, Cloud Storage, and Pub/Sub.
- Cloud Composer (Airflow), Cloud Functions, and Cloud Run.
- Vertex AI integrations.
- IAM and security services.
- Cloud-native design patterns and production operations on GCP.
DataOps:
- CI/CD for data pipelines.
- Infrastructure as Code.
- Automated testing and release management.
- Monitoring, logging, and alerting, building systems teams can operate reliably at scale.
Enterprise integration:
- REST APIs, messaging systems, event-driven architectures, and enterprise applications.
- Kafka, Pub/Sub, Event Hub, or similar technologies.
- Relational and NoSQL databases.
- Hands-on integration with real enterprise environments and an understanding of their operational constraints.
AI and ML platform enablement:
- Data foundations for Vertex AI and machine learning workloads.
- Feature engineering pipelines and model-serving integrations.
- Support for GenAI, RAG, vector search, and agentic AI systems.
AI-assisted engineering:
- Daily use of Copilot, Cursor, Claude Code, Gemini Code Assist, or similar tools.
- A deliberate approach to AI-assisted development, testing, debugging, and documentation, and the ability to describe your workflow in practical detail.
- Ability to teach effective AI-assisted engineering practices to others.
Communication:
- Clear technical writing and documentation.
- Architecture presentations and stakeholder management.
- Comfortable discussing technical trade-offs with client teams and executives.

Nice to Have

Background in a cloud-native engineering team, data platform organization, consulting business, or product company operating large-scale data systems.
Hands-on experience with Databricks and Spark optimization.
Side projects, certifications, open-source contributions, or experiments that demonstrate curiosity.

Additional Information

What we value:
- You naturally discuss trade-offs (e.g., "We chose BigQuery instead of a self-managed warehouse because it reduced operational complexity and improved scalability").
- You talk openly about engineering failures and what you learned from them.
- You ask sharp questions about business outcomes before proposing technical solutions.
- You give credit to teammates easily.
What success looks like:
- First 30 days: you understand the client's data ecosystem, identify meaningful improvements, and take ownership of a pipeline or platform component.
- First 90 days: you independently own a production workload, improve performance or reliability, and actively contribute during client discussions.
- At 12 months: you own a platform capability that multiple teams rely on, have helped develop at least one junior engineer, and clients seek your input on architecture decisions.

What We Offer

Competitive USD Compensation — Get paid in USD.
100% Remote Work — Work from anywhere in Mexico.
Equipment Provided — We provide the equipment you need to do your best work.
Paid Time Off — Enjoy 12 PTO days per year to rest, recharge, and take time for yourself.
U.S. Public Holidays — Celebrate all official public holidays in the United States.
Birthday Off — Enjoy your special day, on us!
Career Growth Opportunities — Keep learning, developing your skills, and advancing your career.
Recognition Program — Your contributions matter, and we make an effort to celebrate them.
Paid Approved Leaves — Receive paid leave for eligible, approved absences, in accordance with company policies.