SAP Datasphere

Responsibilities

• Design and implement reliable SAP Datasphere replication flows to provide accurate, timely synchronization of scientific and enterprise data across multiple source systems and destination layers
• Create and enhance SAP Datasphere dataflows that transform raw laboratory and research inputs into curated datasets for downstream analytics and reporting
• Develop scalable SAP Datasphere data models for advanced Cheminformatics and Bioinformatics scenarios, ensuring consistent performance and end-to-end traceability
• Configure and refine data pipelines that consolidate experimental outcomes, chemical structures, biological sequences, and external reference data into cohesive, domain-specific data assets
• Work closely with Cheminformatics and Bioinformatics domain experts to convert scientific requirements into technical data models and transformation logic that follows best practices
• Assess existing data integration approaches to detect performance constraints, then deliver improvements that decrease processing time and strengthen reliability
• Embed robust data validation and quality checks within SAP Datasphere flows so scientific and operational stakeholders can rely on the accuracy of delivered data
• Coordinate with analytics and application teams to deliver structured data marts and views that support advanced modelling dashboards and scientific decision support capabilities
• Document data architecture, lineage, and transformation logic in a clear, reusable format to improve maintainability, enable knowledge sharing, and support onboarding of new team members
• Apply secure data handling measures to safeguard sensitive research information and regulatory-relevant data while still enabling efficient collaboration across global teams
• Diagnose replication and dataflow/model issues using systematic root cause analysis and implement long-lasting corrective actions
• Drive continuous improvement by evaluating new SAP Datasphere capabilities and proposing incremental enhancements to increase automation and reduce manual effort
• Support hybrid collaboration by taking part in virtual ceremonies and on-site workshops to maintain alignment on project timelines, quality expectations, and stakeholder outcomes

Qualifications

• Professional experience designing SAP Datasphere replication flows with a focus on reliability monitoring and operational readiness for enterprise-scale data landscapes
• Strong capability in building SAP Datasphere dataflows, including complex transformations, joins, aggregations, and reusable components for modular design
• Hands-on experience in SAP Datasphere data modelling, including relational models, semantic layers, star schemas, and object reuse to support analytical and scientific workloads
• Practical exposure to Cheminformatics, including structured handling of molecular descriptor datasets, compound libraries, assay results, and related metadata within data platforms
• Experience with Bioinformatics use cases such as sequence data, data annotations, experimental results integration, and mapping to standardized taxonomies in enterprise systems
• Solid knowledge of SQL, data warehousing concepts, and dimensional modelling to create high-performing, maintainable datasets for reporting and advanced analytics solutions
• Utilize experience with data quality management, including profiling and cleansing, to ensure 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, align priorities, and manage expectations
• Adapt quickly to new tools and processes in SAP cloud environments while adhering to organizational standards for coding, documentation, testing, and deployment
• Demonstrate strong problem-solving skills by independently analyzing complex data issues, proposing options, and delivering solutions within agreed timelines and quality thresholds
• Maintain awareness of regulatory and compliance considerations tied to scientific data and incorporate these requirements into data design, retention, and access control strategies
• Improve team outcomes by participating in code reviews, knowledge-sharing sessions, and process retrospectives that support continuous improvement in data development practices