Data Engineer - LATAM only!

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Data EngineerData

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Quick Summary

Requirements Summary

BigQuery Bigtable Parquet Docker Experience building and maintaining production-grade ETL/ELT pipelines. Strong SQL skills and experience working with large-scale datasets.

Technical Tools

Data EngineerData

What We Offer

~1 min read

Our client, Alloy Mining Intelligence, is building AI-powered systems that help mining operations optimize production and make better real-time decisions through industrial data and machine learning. The team combines deep expertise in mining operations, industrial data infrastructure, and applied AI to deliver measurable improvements across mining processes.

They are looking for a hands-on Data Engineer who can bridge the gap between operational mining systems and modern cloud-based data platforms.

This role is ideal for someone who enjoys solving real-world industrial data challenges.

You'll work directly with data generated from mining operations, integrating historian systems, sensor data, and operational datasets into cloud-native platforms that support advanced analytics and machine learning.

The work goes beyond traditional ETL. You'll help build the data backbone that enables optimization models and operational recommendations to flow back into industrial systems used by mine operators.

Responsibilities

~1 min read

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Design and maintain data pipelines that ingest industrial and historian data into GCP.
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Process and transform large-scale Parquet datasets generated from mining operations.
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Load and optimize data within BigQuery and Bigtable environments.
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Build reliable data integration workflows between operational technology (OT) systems and cloud infrastructure.
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Support machine learning teams by providing clean, accessible, production-ready datasets.
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Help operationalize recommendations generated by analytics and ML systems back into historian or control environments.
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Collaborate closely with Data Scientists, Engineers, and mining domain experts.
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Troubleshoot data quality, reliability, and performance issues across the data platform.

Requirements

~1 min read

5+ years of experience in Data Engineering or Data Platform Engineering.
Strong experience with Google Cloud Platform (GCP).
Hands-on experience with:
- BigQuery
- Bigtable
- Parquet
- Docker
Experience building and maintaining production-grade ETL/ELT pipelines.
Strong SQL skills and experience working with large-scale datasets.
Experience integrating data from multiple operational systems.
Ability to work independently in a remote environment.

Nice to Have

~1 min read

Experience in Mining, Metals, Energy, Manufacturing, Industrial IoT, or Process Industries.
Experience working with:
- Industrial historians (OSIsoft PI, AVEVA Historian, Canary, etc.)
- SCADA systems
- Process control systems
- Time-series industrial data
Experience supporting ML or AI-driven products.
Familiarity with industrial operations and production optimization workflows.
Spanish proficiency is a plus.

Direct impact on real-world mining operations.
Work at the intersection of industrial systems, cloud infrastructure, and AI.
Collaborate with a highly specialized team focused on operational optimization.
Opportunity to solve complex data integration challenges rarely seen in traditional SaaS environments.
Fast hiring process with direct access to technical and executive leadership.

Professional background review
Targeted technical deep dive

Conversation with the VP of Data Science

Fast turnaround after final interview

Remote across South America. Candidates with mining industry experience from Chile, Peru, Brazil, Argentina, or other mining-focused regions are especially encouraged to apply.