Lead Data Engineer: Snowflake, Dataiku & AWS Pipelines

EPAM Systems; København; Hybrid; DKK 800,000 - 1,100,000; Full time

Job summary

EPAM Systems in Copenhagen, Denmark, offers a hybrid data engineering role focused on building scalable data pipelines and analytics workflows using Snowflake and Dataiku. You will optimize SQL for transformations, develop Snowflake data models, and create reusable Dataiku features for ML models in collaboration with data science and product teams.

The role emphasizes data quality, governance, and production readiness, with experience in CI/CD and AWS services to troubleshoot pipelines across

Qualifications

• 6–8+ years of enterprise-scale data engineering experience.
• Hands-on with Snowflake and Dataiku.
• Strong SQL skills with optimization experience.
• Python proficiency for data engineering workflows.
• Experience building production data pipelines and data models.
• Familiar with automated testing, monitoring and CI/CD.

Responsibilities

• Build and maintain scalable data pipelines and analytical workflows using Snowflake and Dataiku.
• Write and optimize SQL for data transformation and feature engineering.
• Develop and maintain Snowflake data models for downstream consumption.
• Create reusable features in Dataiku for ML models.
• Implement testing, monitoring and CI/CD for production pipelines.
• Ensure data quality and governance in enterprise environments.
• Troubleshoot data issues across pipelines and AWS services.
• Collaborate with data science, product and delivery teams to translate needs into data solutions.

Skills

Data Engineering

SQL

Python

Production pipelines

Data modeling

Problem solving

Collaboration

Quality & CI/CD

Tools

Snowflake

Dataiku

AWS (Lambda, S3, Glue, Step Functions)

Job description

EPAM Systems in Copenhagen, Denmark, offers a hybrid data engineering role focused on building scalable data pipelines and analytics workflows using Snowflake and Dataiku. You will optimize SQL for transformations, develop Snowflake data models, and create reusable Dataiku features for ML models in collaboration with data science and product teams.

The role emphasizes data quality, governance, and production readiness, with experience in CI/CD and AWS services to troubleshoot pipelines across