• Own Reporting Reliability & Data Quality Ensure dashboards and reports are accurate, reliable, and always available Implement monitoring, alerting, and SLAs for critical reporting assets Investigate and resolve data issues with a clear root-cause analysis • Build Scalable Data Models Design and optimize SQL transformations and data models Improve the performance of datasets and reporting queries Reduce duplication by centralizing business logic in the data layer • Deliver High-Impact BI Solutions Build and maintain dashboards, reports, and analytical tools Translate business needs into scalable BI solutions Deliver projects with clear estimation and predictable execution • Ensure Data Trust & Observability Implement data validation checks and anomaly detection Proactively identify issues before they impact stakeholders Improve overall data quality across pipelines and reporting • Partner with the Business Act as a trusted partner for Marketing and commercial teams Define and maintain key metrics (CAC, ROAS, conversion funnels, etc.) Generate insights that directly influence business decisions

Role Description As a Fullstack Engineer (Semantic / Analytics) (m/f/d), you will own the business meaning of data and make it reusable across analytics, BI, APIs, semantic access, and AI-powered experiences. You will work across semantic modeling, KPI logic, reusable data models, business-facing data exposure, and collaborate closely with platform, AI, solution, and business teams. The platform will continue to evolve toward broader support for unstructured data and lakehouse-style capabilities. We work spec-driven, use AI-assisted engineering tools such as Claude Code and Cursor, follow “You build it, you run it”, and expect strong specialization combined with fullstack ownership. - Build the semantic foundation for data products - 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 - Make curated data usable across BI, APIs, and AI - 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 reliable, fresh, and governed data - 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 - Collaborate across platform, product, and engineering - 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 Qualifications - 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 Requirements - 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 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 - Inspiring office spaces in the heart of Munich - Flexible remote work from home or anywhere within Germany - 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 - 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

• Atuar diretamente em tribos que necessitam de construção de pipelines e novos dash's. • **Experiência Sólida como Engenheiro(a) Analítico(a)**, Engenheiro(a) de BI ou função similar. • Tempo de Experiência Mínimo Comprovado: Entre 2 e 5 (obrigatório). • **Domínio de SQL:** Conhecimento avançado em estruturas, otimização de *queries* e modelagem dimensional/relacional. • **Experiência Avançada com DBT:** Uso profissional na construção, testes e documentação de *pipelines* de transformação de dados. • **Experiência com Airflow:** Construção, manutenção e orquestração de *pipelines* complexos de dados. • **Google Cloud Platform (GCP):** • **Expertise em BigQuery:** Conhecimento aprofundado em performance, *cost management*, e recursos avançados da plataforma. • Familiaridade com outros serviços de dados do GCP (ex: Google Cloud Storage). • **Experiência com Plataformas de BI/Visualização:** Proficiência no desenvolvimento de *dashboards* no **Looker Platform**.

• Work directly within tribes requiring the development of data pipelines and new dashboards. • Strong experience as an Analytics Engineer, BI Engineer, or in a similar role. • Minimum proven experience: 5 to 6 years (required). • Advanced SQL: deep knowledge of schema design, query optimization, and dimensional/relational modeling. • Advanced experience with DBT: professional use in building, testing, and documenting data transformation pipelines. • Experience with Airflow: building, maintaining, and orchestrating complex data pipelines. • Google Cloud Platform (GCP): • Expertise in BigQuery: in-depth knowledge of performance tuning, cost management, and advanced platform features. • Familiarity with other GCP data services (e.g., Google Cloud Storage). • Experience with BI/visualization platforms: proficiency in developing dashboards on the Looker Platform.