Sr Data Architect - Commercial

The position is at Bayer Consumer Health within the Data Assets, Analytics & AI department, focusing on enabling digital transformation by creating analytical solutions to support data-driven decision making and performance optimization in the consumer health sector.

• Bachelor/Master degree in Computer Science, Engineering, or similar.
• 5+ years experience in Data & Analytics, preferably in the CPG industry.
• Excellent data architecture and technology knowledge; data engineering skills in spark-based data processing with Azure Data Factory, Azure Databricks, SQL, Python; familiarity with Snowflake.
• Proficient in data modeling techniques and mentoring data engineers.
• Knowledge of reporting solutions such as Tableau or PowerBI, and data management (cataloguing, quality, compliance).
• Experience with the GitHub ecosystem, including VCS, GitHub Actions, and Azure DevOps.
• Experience delivering data as a product and leading data engineering teams.
• Knowledge of commercial domain data (CRM, eComm, Sell-Out etc.).
• Experience with Agile methodologies (Scrum, Kanban).
• Strong problem solving, analytical, interpersonal, communication, consulting, and presentation skills.
• Fluent in English, with intercultural awareness and willingness to travel occasionally.

• Own & lead technical implementation and management of well-integrated, enriched, and reusable data models and analytics products.
• Collect and manage data requirements of data consumers, translate them into technical concepts considering reusability & cost efficiency, and partner with data providers.
• Ensure best-practice data management and quality assurance processes are defined and applied; continuously enhance implementation frameworks.
• Collaborate with other data architects, analytics leads, and data scientists to reuse data and ensure sustainable pipelines and analytical products.
• Work with global & regional business stakeholders and IT teams to ensure delivery aligns with business priorities and IT objectives.
• Lead integration of different data sources to develop globally harmonized data models & KPIs meeting market requirements.
• Steer delivery teams, provide cost and time estimations, challenge effort estimations, assure high-quality delivery and testing, and manage proper handover to support.
• Contribute to best-practice exchange in data architecture and continuous enhancement of data analytics technology stack.