Lead/Staff Data Engineer (AI/ML Platforms & Data Pipelines)

Recruitis • Thailand

Relocation

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AI Summary

Lead a high-impact team designing scalable data pipelines, ML platforms, and tools for rapid experiment deployment at massive scale. Own end-to-end systems ensuring data quality, accuracy, and efficiency while collaborating with data science and engineering teams. Requires 6+ years of data/ML engineering expertise with deep technical leadership in Python, SQL, and JVM languages.

Key Highlights

Hands-on technical leadership role at the intersection of data engineering, ML engineering, and backend engineering

Ownership of systems enabling production-grade experiment launches at scale with focus on quality, speed, and efficiency

Collaboration with data science and business teams to automate large-scale data management and ML workflows

Key Responsibilities

Provide technical leadership to improve scalability, quality, accuracy, speed, and efficiency of data and ML processes

Design, build, and test tools, libraries, pipelines, and platforms supporting Data Science and Business teams in launching experiments in production

Take end-to-end ownership of systems enabling rapid experiment launches in production environments

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Technical Skills Required

Python SQL Scala

Benefits & Perks

Relocation sponsorship provided

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Nice to Have

Good understanding of Hadoop ecosystems and Big Data architecture

Experience with Scala/Scala Spark development

Linux systems administration skills

Job Description

Our client is a large-scale technology organisation with a strong focus on engineering, data, and AI. They are continuing to invest in AI/ML platform capabilities that enable teams to build, operate, and scale machine learning and intelligent applications more effectively.They are hiring a Lead/Staff Data Engineer to join a high-impact team sitting at the intersection of data engineering, business analytics, ML engineering, and backend engineering. The team builds the pipelines, platforms, and tools that let the Data Science and Performance Marketing teams run experiments in production at scale.

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