Senior Data Engineer — MLOps & Feature Store Architect

EPAM Systems; Bogotá ciudad; In loco; COP 120.000.000 - 240.000.000; Tempo pieno

Vantaggi offerti da questo lavoro

Healthcare benefits

LinkedIn Learning access

Global career opportunities

Descrizione del lavoro

EPAM Systems in Bogotá seeks a Senior Data Engineer to build an MLOps platform, enabling automated model delivery and scalable feature engineering. You will evolve the featureOps stack using Azure Databricks, Azure Data Factory, and Azure DevOps to run CI/CD into the Databricks Feature Store for Model Lab use cases.

Responsibilities include migrating Spark pipelines to Azure, collaborating on forecasting models, and bringing ML models into production while ensuring reliable, scalable data

Competenze

• Hands-on experience with PySpark
• Solid Data Science core skills
• Proven track record in feature engineering and ML pipelines
• Practical experience migrating data pipelines to cloud environments
• English proficiency at B2 level or higher

Mansioni

• Migrate Spark feature engineering pipelines to Azure
• Define and build a forecasting model with the team
• Take developed ML models into production
• Design and implement CI/CD for Databricks Feature Store
• Collaborate with Data Scientists and engineers on feature needs
• Maintain reliability and scalability of data pipelines
• Diagnose and improve Spark-based workflows during migration

Conoscenze

PySpark

Data Science

Feature engineering

ML pipelines

Cloud migrations

Strumenti

MLflow

Docker

Azure Kubernetes Services

Azure Data Factory

Azure Databricks

Descrizione del lavoro

EPAM Systems in Bogotá seeks a Senior Data Engineer to build an MLOps platform, enabling automated model delivery and scalable feature engineering. You will evolve the featureOps stack using Azure Databricks, Azure Data Factory, and Azure DevOps to run CI/CD into the Databricks Feature Store for Model Lab use cases.

Responsibilities include migrating Spark pipelines to Azure, collaborating on forecasting models, and bringing ML models into production while ensuring reliable, scalable data