Team Lead Data Engineering - Snowflake / AWS

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

You will architect and optimize scalable data pipelines, datasets and analytical workflows at EPAM in Copenhagen, Denmark, working in a hybrid model. Initially at one of our Clients in the Advanced Manufacturing / Life Sciences space. Using Snowflake, Dataiku, SQL and Python, you enable feature engineering for machine learning and support robust data products for analytics and business intelligence. You collaborate with data science, product and delivery teams to deliver production-ready solutions with a focus on data quality, performance and maintainability.

Responsibilities

• Build and maintain scalable data pipelines and analytical workflows using Snowflake and Dataiku
• Write and optimize SQL for data transformation, feature engineering and analytical use cases
• Develop and maintain data models, datasets and views in Snowflake for downstream consumption
• Create, validate and maintain reusable features in Dataiku for machine learning models
• Leverage existing features and datasets to reduce duplication and improve consistency
• Implement automated testing, monitoring and CI/CD for production data pipelines
• Ensure data quality and follow enterprise data governance and security standards
• Troubleshoot data issues across pipelines, including upstream AWS services
• Support data requirements for analytics, reporting and business intelligence
• Collaborate with data science, product and delivery teams to translate business needs into reliable data solutions

Requirements

• 6-8+ years of Data Engineering experience at Enterprise scale
• Hands-on experience with Snowflake and Dataiku
• Strong SQL skills, including query optimization and data transformation
• Proficiency in Python for data engineering workflows
• Experience building and maintaining production data pipelines
• Solid understanding of data modeling and feature engineering for analytics and machine learning
• Experience with automated testing, monitoring and CI/CD for data pipelines
• Familiarity with AWS data services, such as Lambda, S3, Glue and Step Functions, for troubleshooting upstream pipelines
• Ability to work effectively within governed enterprise environments
• Strong problem-solving skills and ability to collaborate across technical and business teams

Nice to have

• Experience in healthcare, life sciences or customer-support analytics
• Familiarity with feature stores and reusable feature management
• Experience supporting business intelligence and reporting datasets