Data Engineer - Snowflake/Dataiku
Summary
Data engineer at EPAM in Copenhagen (hybrid) building and optimizing scalable data pipelines, datasets and analytical workflows with Snowflake, Dataiku, SQL and Python. Includes ML feature engineering, CI/CD and monitoring for pipelines, and troubleshooting upstream AWS services, initially for a client in advanced manufacturing/life sciences.
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