SAP Datasphere

Responsibilities

• Design and implement robust SAP Datasphere replication flows that ensure accurate and timely synchronization of scientific and enterprise data across multiple source systems and target layers
• Develop and optimize SAP Datasphere dataflows that efficiently transform raw laboratory and research data into curated datasets ready for downstream analytics and reporting
• Build scalable SAP Datasphere data models that support complex Cheminformatics and Bioinformatics use cases while maintaining performance consistency and traceability
• Configure and refine data pipelines that integrate experimental results chemical structures biological sequences and external reference data into unified domain specific data assets
• Collaborate with domain experts in Cheminformatics and Bioinformatics to translate scientific requirements into technical data models and transformation logic aligned with best practices
• Analyze existing data integration solutions to identify performance bottlenecks and implement enhancements that reduce processing time and improve reliability
• Implement rigorous data validation and quality checks within SAP Datasphere flows to ensure that scientific and operational stakeholders can trust the accuracy of delivered data
• Coordinate with analytics and application teams to provide well structured data marts and views that enable advanced modelling dashboards and scientific decision support tools
• Document data architectures data lineage and transformation logic in a clear reusable manner to support maintainability knowledge sharing and onboarding of new team members
• Apply secure data handling practices to protect sensitive research data and regulatory relevant information while still enabling efficient collaboration across global teams
• Troubleshoot issues in data replication dataflows and models by performing systematic root cause analysis and implementing sustainable corrective actions
• Engage in continuous improvement activities by evaluating new SAP Datasphere features and proposing incremental enhancements that increase automation and reduce manual interventions
• Support hybrid work collaboration by participating in virtual ceremonies and on site workshops to ensure alignment with project timelines quality expectations and stakeholder outcomes

Qualifications

• Possess professional experience in designing SAP Datasphere replication flows with a focus on reliability monitoring and operational readiness for enterprise scale data landscapes
• Demonstrate strong skills in developing SAP Datasphere dataflows including complex transformations joins aggregations and reusable components that support modular designs
• Bring hands on experience in SAP Datasphere data modelling covering relational models semantic layers star schemas and object reuse to support analytical and scientific workloads
• Exhibit practical exposure to Cheminformatics including handling molecular descriptor datasets compound libraries assay results and related metadata within structured data platforms
• Show experience in Bioinformatics data scenarios such as sequence data annotations experimental results integration and mapping to standardized taxonomies within enterprise systems
• Apply sound knowledge of SQL data warehousing concepts and dimensional modelling to design performant and maintainable datasets used by reporting and advanced analytics solutions
• Utilize experience with data quality management profiling and cleansing methods to ensure that scientific and business users receive consistent complete and validated information
• Communicate effectively with cross functional stakeholders such as scientists analysts and product teams to clarify requirements negotiate priorities and manage expectations
• Adapt quickly to new tools and processes within SAP cloud environments while following organizational standards for coding documentation testing and deployment
• Display strong problem solving capabilities by independently analyzing complex data issues proposing options and implementing solutions within agreed timelines and quality thresholds
• Maintain awareness of regulatory and compliance considerations relevant to scientific data and embed these constraints into data design retention and access control strategies
• Contribute to team outcomes by participating in code reviews knowledge sharing sessions and process retrospectives that drive continuous improvement of data development practices