Data Engineer, AI & Data Platforms

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.
• Local Public Holidays — Celebrate all official holidays in your country.
• 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.