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1017- Senior Data Engineer / Machine Learning Engineer at GoFasti. . Location: Remote. We Make Remote Work Remarkable. • TopTalent from LatAm. . Hello! We are . GoFasti, . a Talent-as-a-Service. . GoFasti bridges the gap between world-class developers and designers from LatAm and first-class companies around the globe. . We need an English-fluent . Senior Data Engineer / Machine Learning Engineer. , based in Latin America, available to work remotely.. We are looking for someone with exceptional communication and relationship-building skills, who embraces changes while maintaining strong attention to detail. An interested and proactive person, who's constantly learning and improving their skills.. . Are you the one we are looking for?. . 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-MLmodels). . 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.. . . Requirements:. . . 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.. . . It´s a Plus:. . . 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.. . . . Compensation:. . . The Salary range offered for this position varies from (USD) $4,000 - $5,500 per month, depending on seniority and skillset.. . This position is for an independent contractor, through a payroll platform.. . The talent will work REMOTELY allocated at our client. . . . Here are the steps for this process:. . Application review/approval > Screening interview with GoFasti's team > Technical Assessment > We build and send your profile to our client > Profile review/approval by client > Interview with the client > . Live coding > . Hiring and onboarding. . .  . . Once you apply for the job, our team will review your resume. If it meets the requirements, we will contact you and move forward in the process. . . Note for Candidates Approached Directly:. If you were contacted directly by a member of our team and are interested in this opportunity, please do not apply through this link. Instead, reach out to the person who contacted you to coordinate a meeting. . Thank you!