We are looking for a Lead Data DevOps Engineer to architect and run reliable data and ML pipelines on the Domino Data Lab platform, ensuring secure and efficient compute and data infrastructure. You will guide best practices across pipeline engineering, Kubernetes operations, and delivery automation. Apply now to help raise platform reliability and speed.

Responsibilities

Lead the design of end-to-end data and ML pipelines on the Domino Data Lab platform
Architect secure and scalable patterns for data ingestion, transformation, validation, and orchestration
Build and maintain Domino projects, environments, datasets, jobs, connectors, and flows to support production workflows
Operate Kubernetes-based workloads for data and ML jobs, including deployment, debugging, and performance tuning
Create and optimize Docker images for reproducible data and ML execution
Implement CI/CD automation for testing, release, and deployment of pipelines and workflow artifacts
Define best practices and guardrails for reliability, security, cost awareness, and reproducibility




Troubleshoot pipeline failures and platform issues, drive root-cause analysis, and implement preventive fixes
Collaborate with stakeholders to translate requirements into technical designs and delivery plans
Mentor engineers on Domino usage, pipeline patterns, and operational excellence
Document architectures, runbooks, and operating procedures for ongoing support
Improve monitoring and operational visibility for data and ML workflows
Ensure solutions align with compliance-minded practices expected in regulated domains

Requirements

5+ years of experience in data software engineering and pipeline delivery
Expert-level Domino Data Lab expertise with Data Sources, Datasets, Environments, Projects, Jobs, and Flows
5+ years of experience with Python for data engineering and automation
Strong SQL skills for extraction, transformation, and optimization
Hands-on Kubernetes experience with workload deployment, debugging,

📌 Lead Data DevOps Engineer (Chile)
🏢 EPAM Systems
📍 Chile