Senior Data Engineer

EPAM Systems; Argentina; Presencial; ARS 134.328.000 - 194.030.000; Jornada completa

Descripción de la vacante

EPAM Systems is seeking a Senior Data Engineer to design, build, and maintain robust data pipelines for analytics and ML workloads. You will champion end-to-end data workflows, from ingestion to delivery, and oversee secure, scalable compute infrastructure.

Collaborating with data scientists and engineers, you will enforce best practices, automate tests and deployments, and monitor performance to ensure data quality and reliability across platforms.

Formación

• Minimum 3 years of relevant experience.
• Hands-on experience with Domino Data Lab platform, incl. Data Sources/Connectors, Datasets, Projects, Flows.
• Expert Python for data engineering and pipeline development.
• Strong SQL across relational and data warehouse systems.
• Experience with Kubernetes and cloud providers, deployment and tuning.
• CI/CD experience for data pipelines and ML workflows.
• Familiarity with GenAI, LLMs, or related tooling from a data pipeline view.
• Knowledge of orchestration tools like Airflow, Domino Flows.

Responsabilidades

• Design, build, and maintain robust data pipelines for analytics and ML workloads.
• Manage end-to-end lifecycle of data workflows from ingestion to delivery.
• Oversee compute infrastructure for secure, efficient platform operations.
• Troubleshoot issues impacting pipeline performance and data quality.
• Collaborate with data scientists and engineers to support tooling needs.
• Establish and promote best practices for platform usage and pipeline design.
• Automate testing and deployment processes to improve reliability.
• Monitor pipeline health and address bottlenecks or failures proactively.
• Document designs, workflows, and configurations for knowledge sharing.

Conocimientos

Python

SQL

Bash

R

Docker

Kubernetes

ETL/ELT pipelines

CI/CD

Cloud

English proficiency

Herramientas

Jenkins

GitLab CI

GitHub Actions

Azure DevOps

Airflow

Domino Flows

Descripción del empleo

Responsibilities

• Design, build, and maintain robust data pipelines that support analytical and machine learning workloads
• Manage the end-to-end lifecycle of data workflows, from ingestion through transformation and delivery
• Oversee compute infrastructure to ensure efficient, secure, and reliable platform operations
• Troubleshoot and resolve issues affecting pipeline performance and data quality
• Collaborate with data scientists and other engineers to support their infrastructure and tooling needs
• Establish and promote best practices for platform usage, pipeline architecture, and data workflow design
• Automate testing and deployment processes to improve reliability and reduce manual effort
• Monitor pipeline health and proactively address bottlenecks or failures
• Contribute to the ongoing improvement of internal tools and processes supporting data operations
• Document technical designs, workflows, and configurations to support knowledge sharing across the team

Requirements

• A minimum of 3 years of relevant experience
• Extensive hands-on experience with the Domino Data Lab platform, including Data Sources and Connectors, Datasets, Environments, Projects, Jobs, and Flows, with the ability to architect and troubleshoot end-to-end data pipelines and guide best practices for platform usage
• Expert-level proficiency in Python as the primary language for data engineering and pipeline development
• Strong command of SQL for data extraction, transformation, and optimization across relational and warehouse systems
• Comfortable working with R and Bash across the broader data science toolchain and for automation scripting
• Demonstrated experience designing, building, and maintaining ETL/ELT pipelines, including data ingestion, transformation, validation, and orchestration
• Hands-on experience with Kubernetes and managed solutions such as EKS, AKS, or GKE, with the ability to deploy, debug, and tune cluster workloads running data and ML jobs
• Experience building, optimizing, and troubleshooting container images for data and ML workloads using Docker
• Experience building and maintaining CI/CD pipelines using tools such as Jenkins, GitLab CI, GitHub Actions, or Azure DevOps to automate testing and deployment of data pipelines and ML workflows
• Working knowledge of at least one major cloud provider, such as AWS, Azure, or GCP, with the ability to reason about data architecture, cost, and security trade-offs
• Excellent English proficiency (B2 level or higher)
• Nice to have
• Experience with Domino Nexus or hybrid/multi-cloud compute orchestration
• Experience delivering ML workflows covering training, deployment, monitoring, and retraining, with an understanding of reproducibility and versioning
• Practical experience with GenAI, LLMs, or agentic frameworks, including retrieval-augmented generation (RAG), from a data pipeline perspective
• Familiarity with orchestration tools such as MLflow, Kubeflow, Airflow, or Domino Flows
• Knowledge of model governance, compliance automation, or audit logging frameworks
• Experience working in pharma, BFSI, or public sector environments