Lead Data DevOps Engineer (Domino Data Lab)

EPAM Systems; Argentina; Presencial; ARS 134.731.000 - 224.551.000; Jornada completa

Descripción de la vacante

EPAM Systems is seeking a Lead Data DevOps Engineer to architect and run reliable data and ML pipelines on the Domino Data Lab platform. You will guide best practices across pipeline engineering, Kubernetes operations, and delivery automation.

You will lead end-to-end design, secure patterns for ingestion and orchestration, and deploy Docker-enabled workloads while driving CI/CD automation and governance across data and ML workflows in a regulated domain.

Formación

• 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, and tuning (including managed offerings).
• Strong containerization skills with Docker image build and troubleshooting.
• Proven CI/CD experience for data and ML pipelines using modern automation tools.
• Strong leadership skills to set standards, mentor peers, and drive technical decisions.
• Strong project ownership skills to plan, prioritize, and deliver end-to-end improvements.
• Strong stakeholder communication skills with a consultative, direct approach.
• Upper-Intermediate English proficiency (B2).
• Experience working in Pharma & Biotech environments.
• Nice to have Amazon Web Services experience for data and ML platform architecture.
• MLOps experience across training, deployment, monitoring, and retraining workflows.
• MLflow experience for experiment tracking and lifecycle coordination.
• Gen AI Application Development experience focused on RAG and data pipelines.

Responsabilidades

• 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.

Conocimientos

Data pipelines

Python

SQL

Kubernetes

Docker

CI/CD

Leadership

Stakeholder comms

English (B2)

MLOps

Gen AI pipelines

Herramientas

Docker

Kubernetes

CI/CD tools

Descripción del empleo

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.

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, and tuning (including managed offerings)
• Strong containerization skills with Docker image build and troubleshooting
• Proven CI/CD experience for data and ML pipelines using modern automation tools
• Strong leadership skills to set standards, mentor peers, and drive technical decisions
• Strong project ownership skills to plan, prioritize, and deliver end-to-end improvements
• Strong stakeholder communication skills with a consultative, direct approach
• Upper-Intermediate English proficiency (B2)
• Experience working in Pharma & Biotech environments
• Nice to have Amazon Web Services experience for data and ML platform architecture
• MLOps experience across training, deployment, monitoring, and retraining workflows
• MLflow experience for experiment tracking and lifecycle coordination
• Gen AI Application Development experience focused on RAG and data pipelines