SAP Datasphere

Responsibilities

• Manage end to end delivery of SAP Datasphere initiatives ensuring replication flow data flow and data modelling workstreams are executed on time within scope and aligned to stakeholder expectations in cheminformatics and bioinformatics domains.
• Coordinate detailed project plans for SAP Datasphere replication flow and data flow pipelines defining milestones dependencies and resource needs to support scientific data integration and analytics.
• Monitor project progress using structured status tracking proactively identifying schedule risks in data modelling or integration tasks and driving timely mitigation actions with technical and domain experts.
• Align project activities with hybrid work model practices balancing on site collaboration for critical design workshops with remote execution to maintain efficiency and work life balance.
• Collaborate closely with data architects SAP specialists and scientific users to translate cheminformatics and bioinformatics requirements into actionable SAP Datasphere configurations and project deliverables.
• Support project governance by organizing review meetings preparing concise progress reports and maintaining transparent communication with sponsors on scope budget and quality aspects.
• Ensure data quality and consistency by coordinating validation activities for replication flows data flows and models that support research datasets and experimental results.
• Partner with domain scientists to prioritize backlog items ensuring that data platform enhancements directly enable discovery predictive modelling and advanced analytics in chemistry and biology research.
• Coordinate with enterprise IT and security teams to align SAP Datasphere solutions with organizational standards on access control compliance and responsible data use in scientific contexts.
• Drive issue resolution by facilitating root cause analysis sessions for problems in data flows or models and coordinating action plans that restore stable operations for research users.
• Support change management by planning user training sessions preparing reference materials and encouraging adoption of new data workflows among cheminformatics and bioinformatics stakeholders.
• Optimize project execution by analyzing lessons learned from previous releases and applying improvements to estimation planning and collaboration practices in subsequent waves.
• Communicate clearly with cross functional teams about priorities expectations and timelines ensuring that technical and scientific contributors remain synchronized throughout the project lifecycle.
• Provide guidance on aligning SAP Datasphere capabilities with long term data platform strategies emphasizing sustainable architecture that can scale with future research and analytics demands.

Qualifications

• Require professional experience of 6 to 8 years in project management with at least 3y ears handling SAP data platform or analytics projects in complex enterprise environments.
• Require hands on exposure to SAP Datasphere replication flow data flow and data modelling activities sufficient to plan tasks estimate effort and engage meaningfully with technical practitioners.
• Require demonstrated experience managing projects in cheminformatics or bioinformatics domains supporting scientific data pipelines research workflows or computational analysis initiatives.
• Require strong understanding of how scientific data such as chemical structures assay results and biological sequences are curated transformed and integrated into analytics ready datasets.
• Require proven ability to manage stakeholders from both technical and scientific backgrounds balancing competing priorities while maintaining focus on outcome driven delivery.
• Require familiarity with hybrid work models including coordination of distributed teams use of collaboration tools and facilitation of remote workshops or standups.
• Require solid knowledge of project management practices including scope management risk management schedule control and status reporting using recognized frameworks.
• Nice to have exposure to agile or iterative delivery methods for data and analytics projects including backlog management and incremental release planning.
• Nice to have understanding of cloud based data platforms and integration patterns that complement SAP Datasphere in enterprise research environments.
• Nice to have experience working in multinational organizations where cross cultural communication and alignment across regions are part of daily responsibilities.
• Nice to have familiarity with regulatory and compliance considerations related to scientific data such as data governance audit readiness and controlled access to sensitive datasets.
• Nice to have professional certification in project management or data related disciplines that demonstrates commitment to continuous learning and structured delivery practices.