• You work on varied client projects and are responsible for implementing modern data-engineering architectures with a focus on Microsoft Fabric. • You support clients from requirements analysis through architectural decisions to implementation and the successful transition into operational production. • In doing so, you build deep expertise in Microsoft Fabric and adjacent cloud & AI technologies — or develop existing knowledge further into true excellence. • You design and implement advanced data platforms based on Microsoft Fabric. • You shape solutions across the entire data value chain — from the cloud platform through integration and transformation processes to analytics and AI applications. • You work closely with clients in workshops, architecture sessions, and strategic roadmaps, guiding them confidently through transformation phases. • You deliver projects using modern Data & AI technologies — including Microsoft Fabric, Databricks, Azure services, and current ML frameworks. • You work with a variety of industries and company sizes across the DACH region, building a broad and valuable range of experience.

• Build analytical data products and provide valuable insights to business stakeholders in innovative leading e-commerce brands • Design and deploy features for our cloud-based Data Lake and our Customer Lifetime Value (CLV) product in an agile, test-driven environment • Design schemas and datalakes and datamarts • Build scalable data-processing pipelines in the cloud using technologies like Apache Spark, Hive, BigQuery, micro-services, Kubernetes • Use Python and SQL to analyze digital marketing performance

• Design, build, and maintain reliable end-to-end ETL pipelines orchestrated with Apache Airflow • Integrate data from multiple sources (internal operational databases, third-party APIs, SaaS tools) into the Google Cloud Data Warehouse (BigQuery) • Design and evolve data models, warehouse schemas, and transformations to support scalable analytics and KPIs • Ensure data quality, reliability, and observability through monitoring, validation, and alerting • Own the product data structure, mapping product features and behaviors to analytics-ready data models • Define and maintain meaningful KPIs in collaboration with Product and BI • Enable analytics for AI-powered product features, ensuring visibility on usage, performance, quality, and business impact • Partner with Product, BI, and other stakeholders to gather requirements and deliver dashboards and reports • Maintain clear and up-to-date documentation for data models, pipelines, and metrics • Act as the primary bridge between Backend Engineering and BI, owning the flow from data production to analytics consumption • Triage, analyze, and address BI requests related to data availability, correctness, performance, and modeling • Collaborate with Backend Engineers on data contracts, schema evolution, and performance optimization, without owning core backend services • Proactively identify and resolve data-related issues impacting BI and Product teams • Own first-level monitoring and support for data pipelines and Airflow DAGs, ensuring timely resolution of failures • Collaborate with BI and Backend teams to troubleshoot and resolve complex issues • Continuously improve the stability, performance, and maintainability of the data platform

• Ensure the definition, governance, and evolution of master data in the company's critical domains, acting as an architectural reference and enabling system-wide consistency, operational efficiency, and decision-making based on reliable data. • Define and evolve the company's Master Data Management (MDM) strategy • Map and prioritize critical master data domains • Define entities, attributes, business rules, and official sources (systems of record) • Design architectures for the creation, update, and consumption flows of master data • Specify integration and synchronization standards (event-driven, APIs, batch) • Define and influence data ownership with business areas • Lead or guide MDM implementation initiatives (tools and processes) • Establish and monitor data quality metrics • Work on identifying root causes of issues (not just symptoms) • Prioritize structural fixes versus manual cleansing • Serve as a reference for squads and architectures in adopting the master data model • Act as a bridge between business, data, and technology