ML Data Engineer

LATAM (Remote)Remotefull-timemid

Data EngineerData

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Quick Summary

Overview

#poweringyouringenuity 🚀 Our mission is to bridge top-level technology companies with engineering talent from across the globe. With presence in LATAM, USA and Europe, we empower companies by providing remote engineering teams of all levels tailored to the needs of each project.

Key Responsibilities

Monitor, investigate, and resolve issues across data ingestion pipelines, and go beyond firefighting to implement lasting solutions.

Requirements Summary

5+ years of experience as a Data Engineer, with a strong background in data infrastructure. Solid problem-solving skills and the ability to juggle multiple priorities without dropping the ball.

Technical Tools

awsazuregcpcode-reviewetlmachine-learning

Responsibilities

~1 min read

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Monitor, investigate, and resolve issues across data ingestion pipelines, and go beyond firefighting to implement lasting solutions.
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Act as a data expert: optimize queries, contribute to data architecture decisions, and help teams get the most out of their data.
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Provide infrastructure support that improves scalability, stability, and security across internal systems.
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Raise the bar for code quality through automation, unit testing, and thoughtful code reviews.
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Document code, design decisions, and data sources so your work is maintainable and your teammates can build on it.
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Collaborate across disciplines to untangle complex data challenges, and enjoy doing it.

5+ years of experience as a Data Engineer, with a strong background in data infrastructure.
Solid problem-solving skills and the ability to juggle multiple priorities without dropping the ball.
Experience designing and maintaining data pipelines with an eye for performance and reliability.
A proactive, ownership-driven attitude, you spot problems before they escalate.
Strong English communication skills (B2/C1). You'll work closely with cross-functional teams and need to articulate both technical and non-technical ideas clearly.

Experience supporting ML platforms or working alongside data science/ML teams.
Familiarity with cloud platforms (AWS, GCP, or Azure).
Knowledge of observability tools, logging, metrics, and tracing.
A track record of improving system reliability through automation and monitoring.

Join us to be part of a dynamic community where your skills and contributions truly matter!