Data & Analytics Engineer

Join EGYM as a Data & Analytics Engineer in Munich or Paris. In this role, you will design and build reliable data pipelines, translate complex business processes into scalable analytical data models, and help build the semantic foundation for the next generation of BI, self-service analytics, and AI-powered data experiences. You will also raise the engineering bar through automated testing, data quality, observability, and performance optimization.

• Design, build, and operate reliable data pipelines and transformations across heterogeneous enterprise source systems, products, applications, APIs, and event-based data.
• Translate complex business processes into scalable analytical data models, define clear grains, facts, dimensions, and relationships, and establish conformed business entities across different systems and domains.
• Help build and evolve the semantic layer with technologies such as Snowflake Semantic Views, creating reusable and governed definitions of business entities, dimensions, metrics, and relationships.

• Data Modeling: You have strong experience with dimensional and analytical data modeling and can confidently reason about grain, facts, dimensions, slowly changing dimensions, relationships, and different modeling patterns
• AI Mindset: You are curious about how AI is changing data engineering and analytics, actively use or explore AI-assisted engineering tools, and understand that AI output is only as trustworthy as the underlying data models and semantic context
• Engineering Discipline: You have experience with version control, pull requests, automated testing, CI/CD, monitoring, observability, and production ownership; Infrastructure-as-Code experience such as Terraform is a plus
• Trusted & Compliant Data: You build with privacy by design — handling personal and health-adjacent data in line with GDPR, implementing access controls, masking, and retention policies across the data platform
• Professional Experience: You have 3+ years of experience in Data Engineering, Analytics Engineering, or a comparable role and have worked on production-grade analytical data platforms
• Modern Data Stack: You bring hands-on experience with Snowflake or a comparable cloud data warehouse, strong dbt experience, and excellent SQL skills; Python and experience with orchestration technologies such as Airflow or Prefect are highly valuable
• Semantic Thinking: You understand why consistent business definitions, metrics, metadata, and semantic models are essential for scalable BI and increasingly for AI-driven analytics; previous experience with Snowflake Semantic Views, dbt Semantic Layer, MetricFlow or similar technologies is a plus
• Collaborative Mindset: You are hands-on, proactive, and able to bridge technical and business perspectives, challenge assumptions constructively, and help teams turn ambiguous business concepts into clear technical solutions
• Enterprise Integration: You have experience integrating data from multiple heterogeneous systems and are comfortable dealing with conflicting identifiers, changing source structures, inconsistent definitions, and complex cross-domain dependencies