Fullstack Engineer (Semantic / Analytics) (m/f/d)

• Build and evolve the semantic layer in dbt and Snowflake semantic views, including business entities, metrics, dimensions, and reusable data models
• Define KPIs, business logic, canonical data definitions, and semantic consistency standards together with business and product stakeholders
• Help shape how semantic data products are exposed consistently across internal and external platform capabilities
• Ensure business entities, KPIs, and metrics are clearly and consistently defined across the platform

• Expose curated data for BI tools such as Amazon QuickSight and Apache Superset, APIs, downstream product use cases, and AI consumption including Snowflake Cortex AI
• Support AI use cases through feature shaping, context structuring, semantic enrichment, and business-grounded data preparation
• Collaborate with the AI Engineer to ensure agentic experiences are grounded in meaningful, well-structured business data
• Help ensure BI, APIs, and AI use cases rely on the same trusted semantic foundations in Snowflake

• Work with near-real-time data ingested from PostgreSQL into Snowflake via Snowflake Openflow
• Ensure semantic models reflect fresh, reliable data from operational systems
• Align with solution teams on data contracts, source semantics, and integration expectations
• Help define validation rules, data trust practices, lineage support, and consistency controls

• Work closely with the Data & Platform Architect and Data & Platform Engineer to build semantic models on reliable, scalable Snowflake foundations
• Collaborate with platform, AI, solution, product, and business-facing teams
• Help the company build a reusable semantic layer that scales with future platform growth
• Apply spec-driven development, AI-assisted engineering workflows, and end-to-end production ownership

• Very strong hands-on SQL skills and broad, deep database knowledge, including data modeling
• Strong hands-on experience with Snowflake, including Snowflake semantic views
• Hands-on experience with dbt at scale for transformations and analytics engineering best practices
• Experience with PostgreSQL as a source for structured business data
• Experience building semantic layers, reusable metrics, canonical data models, analytics engineering assets, KPIs, business logic, and data definitions with stakeholders
• Experience exposing data for BI, APIs, downstream product use cases, and AI or analytics consumption
• Experience defining or supporting data contracts, validation rules, semantic consistency standards, data quality, lineage, and trust practices

• Experience with near-real-time or CDC ingestion, ideally Snowflake Openflow or comparable tools such as Fivetran, Debezium, or Kafka
• Strong Python skills
• Experience building APIs and services such as REST or GraphQL
• Experience exposing data and tools through interfaces such as MCP servers
• Solid AWS stack know-how
• Experience with BI tools such as Amazon QuickSight, Apache Superset, Looker, Tableau, Power BI, or similar platforms
• Strong understanding of how data should be structured for AI, analytics, semantic access, and product consumption
• Comfortable with structured, spec-driven delivery and AI-assisted development workflows

• Fullstack ownership mindset with the ability to own capabilities from design and implementation to production operations
• Strong specialization in semantic and analytics engineering combined with the willingness to contribute across adjacent layers
• Ability to translate business meaning into reliable, reusable, and scalable data products
• Strong quality mindset around semantic consistency, validation, data trust, and long-term maintainability
• Comfortable working in an evolving platform environment that may expand toward unstructured data and lakehouse-style capabilities

• Ability to work closely with platform, AI, solution, product, and business-facing teams
• Strong collaboration skills for aligning on contracts, source semantics, KPI logic, and integration expectations
• Clear communication style when translating technical data models into business meaning
• Comfortable collaborating with Data & Platform Architecture, Data Engineering, AI Engineering, and solution teams
• Fluent English skills for working effectively in an English-speaking environment

• Some AI agent engineering experience, ideally with Amazon Bedrock AgentCore or frameworks such as LangChain, LangGraph, or Deep Agents
• Some data science experience, such as statistics, feature engineering, experimentation, or machine learning
• Experience with Snowflake Cortex AI
• Experience supporting AI use cases through feature engineering, context shaping, or semantic enrichment
• Experience with data quality, lineage, and validation practices
• Experience in multi-team SaaS environments
• Experience with unstructured data enrichment or hybrid structured and unstructured semantic models
• General understanding of Java and/or frontend React

Purpose & Impact

• A role with real meaning that is both enjoyable and impactful

• The opportunity to make a sustainable contribution through your work

• Attractive compensation as part of a growing company

Attractive Benefits

• 30 days of paid vacation

• EGYM Wellpass membership to support your work-life balance

• Flexible working models to better balance work and personal life

Modern Work Environment & Flexibility

• Inspiring office spaces in the heart of Munich

• Flexible remote work from home or anywhere within Germany

Great Team

• A professional, welcoming, and highly motivated team

• Collaboration at eye level with an open feedback culture

• An environment where people support each other and grow together

Flat Hierarchies & Ownership

• Short decision-making paths and real opportunities to shape things

• Freedom to contribute and implement your own ideas

• A high level of ownership and responsibility