SAP Datasphere

Responsibilities

• Lead end-to-end delivery of SAP Datasphere initiatives, ensuring replication flow, data flow, and data modelling workstreams are completed on time, within scope, and aligned with stakeholder expectations in cheminformatics and bioinformatics domains.
• Develop and coordinate detailed project plans for SAP Datasphere replication flow and data flow pipelines, including milestones, dependencies, and resource requirements to enable scientific data integration and analytics.
• Track project progress through structured status monitoring, proactively identifying schedule risks in data modelling and integration tasks and driving timely mitigation with technical and domain experts.
• Execute project work in line with a hybrid work model, balancing on-site collaboration for critical design workshops with remote delivery to maintain efficiency and support work-life balance.
• Work closely with data architects, SAP specialists, and scientific users to translate cheminformatics and bioinformatics requirements into actionable SAP Datasphere configurations and project deliverables.
• Provide project governance support by organizing review meetings, preparing clear progress reports, and maintaining transparent communication with sponsors regarding scope, budget, and quality.
• Ensure data quality and consistency by coordinating validation activities across replication flows, data flows, and data models that underpin research datasets and experimental results.
• Partner with domain scientists to prioritize backlog items, ensuring that data platform enhancements directly support discovery, predictive modelling, and advanced analytics across chemistry and biology research.
• Coordinate with enterprise IT and security teams to align SAP Datasphere solutions to organizational standards for access control, compliance, and responsible use of data 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 promoting adoption of new data workflows among cheminformatics and bioinformatics stakeholders.
• Improve project execution by capturing lessons learned from prior releases and applying enhancements to estimation planning and collaboration practices in subsequent delivery waves.
• Communicate effectively with cross-functional teams about priorities, expectations, and timelines, ensuring technical and scientific contributors stay aligned throughout the project lifecycle.
• Provide guidance on aligning SAP Datasphere capabilities with long-term data platform strategies, focusing on sustainable, scalable architecture that can expand with future research and analytics needs.

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.