Data & Analytics Engineer (m/f/d)

Join EGYM as our Data & Analytics Engineer – Data Modeling, Semantic Layer & AI Enablement (m/f/d) in Munich or Paris! In this pivotal role, you will help evolve the data foundation that powers EGYM Wellpass and EGYM Technology. You will turn complex data landscapes into scalable, trusted data models and help build the semantic foundation for the next generation of BI, self-service analytics, and AI-powered data experiences. If you combine strong engineering fundamentals with excellent data modeling skills and curiosity for semantic layers and AI, this is your opportunity to make a significant impact.

• Data Engineering & Integration: You design, build, and operate reliable data pipelines and transformations across heterogeneous enterprise source systems, products, applications, APIs, and event-based data, using Snowflake, dbt, GCP, and modern orchestration technologies, in a warehouse-centric environment
• Enterprise Data Modeling: You 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
• Semantic Layer: You help build and evolve our semantic layer with technologies such as Snowflake Semantic Views, creating reusable and governed definitions of business entities, dimensions, metrics, and relationships that can serve BI, self-service analytics, applications, and AI
• AI-Ready Data: You shape data models, metadata, descriptions, relationships, and semantic context so that AI-powered analytics and agents can interact with our data accurately and reliably, and experiment with technologies such as Snowflake Cortex Analyst and Cortex Agents
• Engineering Excellence: You raise the engineering bar through automated testing, data quality, observability, data contracts, CI/CD, documentation, performance optimization, and reliable production ownership
• Modern Engineering & AI: You actively use AI-assisted development to improve engineering productivity and explore where new technologies can simplify our stack, automate repetitive work, or enable completely new ways of interacting with data
• Shared Ownership: You participate in architectural decisions, challenge existing solutions, grow alongside other engineers, and contribute to scalable engineering and modeling standards across the team- 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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 platform1. Talent Acquisition Interview - 30mins

2. Technical interview - 90mins
3. Peer interview to meet teammembers - 90mins