SAP Datasphere

Sr Developer position at an MNC delivering SAP Datasphere solutions for Cheminformatics and Bioinformatics. In this hybrid work model, the candidate will design and optimize data models, dataflows, and replication flows to enable scientific analytics. The role requires close collaboration with cross-functional teams to deliver dependable, secure enterprise data products.

Responsibilities

• Design and build robust SAP Datasphere replication flows to ensure precise and timely synchronization of scientific and enterprise data across multiple source systems and target layers
• Develop and optimize SAP Datasphere dataflows to efficiently transform raw laboratory and research data into curated datasets for downstream analytics and reporting
• Create scalable SAP Datasphere data models for advanced Cheminformatics and Bioinformatics use cases, maintaining performance consistency and end-to-end traceability
• Configure and enhance data pipelines that consolidate experimental results, chemical structures, biological sequences, and external reference data into unified, domain-specific data assets
• Work closely with Cheminformatics and Bioinformatics domain experts to convert scientific requirements into technical data models and transformation logic in line with established best practices
• Evaluate existing data integration solutions to uncover performance bottlenecks, then implement improvements that lower processing time and increase reliability
• Embed rigorous data validation and quality controls in SAP Datasphere flows to ensure scientific and operational stakeholders trust the accuracy of delivered data
• Partner with analytics and application teams to deliver well-structured data marts and views that support advanced modelling dashboards and scientific decision support tools
• Document data architecture, data lineage, and transformation logic in a clear, reusable format to improve maintainability, support knowledge sharing, and enable onboarding for new team members
• Apply secure data handling practices to protect sensitive research data and regulatory-relevant information while still enabling effective collaboration across global teams
• Troubleshoot problems in data replication, dataflows, and models using systematic root cause analysis and by implementing long-lasting corrective actions
• Participate in continuous improvement by assessing new SAP Datasphere capabilities and proposing incremental enhancements that 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 emphasis on reliability monitoring and operational readiness for enterprise-scale data landscapes
• Strong expertise in developing SAP Datasphere dataflows, including complex transformations, joins, aggregations, and reusable components that enable 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 working with molecular descriptor datasets, compound libraries, assay results, and related metadata within structured data platforms
• Experience with Bioinformatics scenarios, such as sequence data annotations, experimental results integration, and mapping to standardized taxonomies within enterprise systems
• Sound knowledge of SQL, data warehousing concepts, and dimensional modelling to produce performant, maintainable datasets for reporting and advanced analytics solutions
• Experience with data quality management, profiling, and cleansing to ensure scientific and business users receive consistent, complete, and validated information
• Effective communication with cross-functional stakeholders such as scientists, analysts, and product teams to clarify requirements, negotiate priorities, and manage expectations
• Ability to quickly adopt new tools and processes in SAP cloud environments while adhering to organizational standards for coding, documentation, testing, and deployment
• Strong problem-solving skills demonstrated through independent analysis of complex data issues, proposing options, and delivering solutions within agreed timelines and quality thresholds
• Awareness of regulatory and compliance considerations for scientific data, and capability to incorporate these constraints into data design, retention, and access control strategies
• Contribution to team results through participation in code reviews, knowledge sharing sessions, and process retrospectives that drive continuous improvement in data development practices