GoFasti · 16 hours ago
1017- Senior Data Engineer / Machine Learning Engineer
United States
Full-time
Remote
Senior Level
$4,000/mo - $5,500/mo
5+ years expGoFasti is a Talent-as-a-Service company that connects top talent from LatAm with leading global firms. They are seeking an English-fluent Senior Data Engineer / Machine Learning Engineer to design and build scalable data ingestion pipelines, create clean datasets for ML training, and develop ML models for various applications.
Human ResourcesInformation TechnologySoftwareSoftware Engineering
No H1B
Responsibilities
Design and build scalable data ingestion pipelines for diverse sources: Grid and market data (e.g., telemetry, operational datasets, filings), Geospatial data (satellite imagery, maps, infrastructure layers), Weather and environmental data, Time-series load and generation data
Create clean, versioned, query-able datasets suitable for both ML training and analytics
Develop canonical data models / schemas representing grid topology and asset relationships
Ensure data quality, lineage, reproducibility, and observability across pipelines
Engineer temporal, spatial, and relational features across heterogeneous datasets
Build representations that capture: Network topology (connectivity, constraints, hierarchy), Time-dependent behavior (load, generation, congestion, weather), Physical constraints and operational limits
Collaborate with physics-based modeling efforts (e.g., power-flow abstractions) and integrate outputs into ML workflows
Train and deploy time-series forecasting models for: Load, Renewable generation (wind, solar), Grid conditions and system stress indicators, Work with multi-horizon forecasting (short-term operational + long-term planning)
Implement models ranging from: Classical statistical methods (when appropriate), Modern ML approaches (deep learning, sequence models, hybrid physics-ML models)
Evaluate models rigorously using real-world performance metrics, not just offline benchmarks
Design end-to-end ML pipelines: Data ingestion → feature generation → training → validation → deployment → monitoring → retraining
Build reliable inference pipelines that support near-real-time and batch workflows
Implement: Model versioning, Automated retraining, Drift detection, Performance monitoring
Work closely with product and platform engineers to integrate ML outputs into customer-facing systems
Qualification
Required
5- 7+ years of experience in data engineering, ML engineering, or applied ML roles
Proven experience deploying ML systems into production (not just notebooks)
Strong background in time-series data (forecasting, anomaly detection, temporal feature engineering)
Deep proficiency in Python and modern data/ML libraries
Experience building scalable data pipelines (batch and streaming)
Strong systems thinking — ability to reason about end-to-end data and model lifecycles
Leadership experience
Preferred
Experience with Databricks, Spark, or similar large-scale data platforms
Geospatial data experience (GIS, raster/vector data, spatial joins, map-based features)
Experience in weather, energy, load forecasting, or infrastructure modeling
Familiarity with MLOps frameworks and best practices
Experience working with messy, real-world datasets and ambiguous problem statements
Exposure to hybrid physics + ML systems or domain-constrained modeling
Company
GoFasti
GoFasti is helping companies scale their teams with world-class talent in Latin America.
51-200 employees
Funding
Current Stage
Growth StageLeadership Team
Pablo Genevo
CEO & Co-Founder
Mateus Rodrigues de Morais
CTO & Co-Founder
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