Lead Data Engineer: Scalable Cloud Pipelines & Automation

EPAM Systems Inc; Argentina; A distancia; ARS 123.879.000 - 185.819.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Healthcare benefits

Paid time off

Learning and development opportunities

Global career opportunities

Descripción de la vacante

EPAM Systems Inc. is seeking a hands-on Lead Data Engineer to build scalable data pipelines on AWS and craft cloud-based data solutions. You will lead development, implement DAGs with Airflow, and drive automation with Terraform and CI/CD via GitHub Actions.

You will apply Python (Pandas) and PySpark to large-scale data processing, coordinate with cross-functional teams in an Agile setup, and ensure robust testing, monitoring, and data quality across production pipelines.

Formación

• 5+ years of relevant professional background.
• Experience leading and managing development teams.
• Strong Python for data engineering tasks (Pandas).
• Proficient PySpark for large-scale data processing.
• Experience with AWS MWAA or Airflow to orchestrate workflows.
• Terraform for infrastructure automation and GitHub Actions for CI/CD.

Responsabilidades

• Create, construct, and maintain scalable data pipelines on AWS.
• Develop data processing solutions using Python, Pandas, and PySpark.
• Manage and monitor workflows with MWAA/Airflow.
• Automate infrastructure setup and deployments with Terraform and GitHub Actions.
• Implement testing, monitoring, and data-quality safeguards.

Conocimientos

Python

PySpark

AWS

Airflow

DevOps

Automation

Leadership

Herramientas

Terraform

GitHub Actions

Airflow

MWAA

Athena

Descripción del empleo

EPAM Systems Inc. is seeking a hands-on Lead Data Engineer to build scalable data pipelines on AWS and craft cloud-based data solutions. You will lead development, implement DAGs with Airflow, and drive automation with Terraform and CI/CD via GitHub Actions.

You will apply Python (Pandas) and PySpark to large-scale data processing, coordinate with cross-functional teams in an Agile setup, and ensure robust testing, monitoring, and data quality across production pipelines.