Team Lead Data Engineering - Snowflake / AWS

Cph Ai Hub; København; Hybrid; DKK 900,000 - 1,200,000; Full time

Job summary

EPAM in Copenhagen, Denmark is seeking a Senior Data Engineer to architect and optimize scalable data pipelines, datasets and analytical workflows in a hybrid model. You will work on client projects in Advanced Manufacturing / Life Sciences, using Snowflake, Dataiku, SQL and Python to enable ML feature engineering and robust data products for analytics and BI.

You will collaborate with data science, product and delivery teams to deliver production-ready solutions with a focus on data quality,

Qualifications

• 6+ 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 ML.
• Experience with automated testing, monitoring and CI/CD for data pipelines.
• Familiarity with AWS data services for troubleshooting upstream pipelines.
• Ability to work effectively within governed enterprise environments.
• Strong problem-solving skills and ability to collaborate across teams.

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 and datasets for downstream use.
• Create, validate and maintain reusable features in Dataiku for ML 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 compliance with enterprise governance and security standards.
• Troubleshoot data issues across pipelines, including upstream AWS services.
• Support data requirements for analytics, reporting and BI.
• Collaborate with data science, product and delivery teams to translate business needs into reliable data solutions.

Skills

SQL

Python

Data Modeling

Data Engineering

Tools

Snowflake

Dataiku

AWS Lambda

AWS S3

AWS Glue

AWS 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