Senior Data Engineer - Machine Learning - GP, Remote: Colombia - Costa Rica, Fulltime

- This position is open to candidates located in Colombia or Costa Rica only -

As a Senior Data Engineer (ML), you will lead the design and implementation of end-to-end data and machine learning solutions from feature engineering to model deployment, working closely with cross-functional engineers in a distributed environment.

This role is hands-on and delivery-focused, with ownership across the full ML lifecycle in a modern GCP-based architecture.

What You’ll Do

• Design and build end-to-end ML pipelines using GCP services (Vertex AI, BigQuery, Dataform)
• Develop and productionize tabular ML models (e.g., XGBoost or similar)
• Implement robust feature engineering pipelines with point-in-time correctness
• Ensure reliable batch scoring workflows and production deployment
• Partner with engineering and product stakeholders to translate business needs into ML solutions
• Optimize data workflows for performance, scalability, and cost efficiency
• Contribute to model evaluation, monitoring, and continuous improvement
• Collaborate within a distributed team, ensuring clear communication and delivery alignment
  

Must-Have Qualifications

• Strong hands-on experience building and deploying ML solutions on GCP (Vertex AI ecosystem)
• Pipelines, Model Registry, Feature Store, Batch Prediction
• Proven track record delivering production ML models (not just experimentation)
• Solid understanding of classification problems, especially imbalanced datasets
• Metrics such as AUC-PR, ROC-AUC, precision@K, calibration
• Experience designing feature pipelines with point-in-time accuracy
• Avoiding data leakage and training/serving skew
• Advanced proficiency in BigQuery
• Partitioning, clustering, and cost-aware query design
• Experience with data transformation tools (Dataform, dbt, or similar)
• Strong Python skills for production systems pandas, scikit-learn, XGBoost, testing practices
• Familiarity with ML lifecycle and MLOps practices
  
• Experiment tracking, model versioning, containerized deployment
  

Nice to Have

• Experience with Vertex AI Model Monitoring (drift and skew detection)
• Ability to translate model outputs (e.g., SHAP values) into actionable business insights
• Experience working with distributed teams across time zones