SAP Datasphere

About Sutherland

Artificial Intelligence. Automation. Cloud engineering. Advanced analytics. For business leaders, these are key factors of success. For us, they’re our core expertise.
We work with iconic brands worldwide. We bring them a unique value proposition through market-leading technology and business process excellence.

We’ve created over 200 unique inventions under several patents across AI and other critical technologies. Leveraging our advanced products and platforms, we drive digital transformation, optimize critical business operations, reinvent experiences, and pioneer new solutions, all provided through a seamless “as a service” model.

For each company, we provide new keys for their businesses, the people they work with, and the customers they serve. We tailor proven and rapid formulas, to fit their unique DNA. We bring together human expertise and artificial intelligence to develop digital chemistry. This unlocks new possibilities, transformative outcomes and enduring relationships.

Sutherland
Unlocking digital performance. Delivering measurable results.

Key Responsibilities

1. Architectural Design & Strategy

   • Define end-to-end data architecture strategy using SAP Datasphere in alignment with the enterprise data roadmap, business requirements, and technology standards.

   • Develop high‑level solutions and architectural blueprints for data ingestion, storage, semantic modelling, virtualization, federation, data cataloguing, and analytics.

   • Ensure the architecture supports scalable integration with both SAP and non‑SAP systems, hybrid and cloud landscapes.

2. Data Modelling & Semantic Layer

   • Design logical and physical data models (including spaces, views, calculation views, CDS views, etc.) that are efficient, maintainable, and reusable.

   • Build and manage semantic layers to provide business‑friendly data definitions and consistent meanings across the organisation.

3. Integration & Data Flows

   • Lead extraction, transformation, load (ETL)/extract‑load‑transform (ELT) pipelines, replication, and virtualization of data.

   • Integrate SAP Datasphere with SAP S/4HANA, BW/BW4HANA, HANA, embedded analytics, third‑party / non‑SAP systems via APIs, OData, connectors, etc.

4. Governance, Security & Compliance

   • Define and enforce data governance policies: metadata management, data catalogues, lineage, versioning.

   • Ensure proper security, access control, authentication, authorization aligned with internal policy and external regulatory requirements (privacy, compliance).

   • Maintain data integrity, quality, reliability, and availability.

5. Performance, Scalability & Operational Excellence

   • Ensure that the implemented Datasphere solutions are performant: query optimization, partitioning, caching, and efficient resource usage.

   • Monitor and tune the systems; diagnose performance bottlenecks.

   • Plan for disaster recovery, backup strategies, capacity planning, high availability.

6. Implementation & Migration

   • Lead migrations from legacy systems/data warehouses to Datasphere (e.g., BW/BW4HANA, older DW systems).

   • Plan and execute pilot, proof of concept (POC), and production rollout.

   • Handle post‑go‑live support and continuous improvement.

7. Stakeholder Management & Advisory

   • Engage with business leaders, analysts, and product owners to understand requirements; translate business needs into technical solutions.

   • Work closely with data engineering, analytics, reporting (SAC etc.), BI teams, cloud teams.

   • Participate in presales / advisory / solution design with clients or internal customers, where applicable.

8. Leadership & Mentoring

   • Lead, mentor, and guide more junior architects, developers, modelers and engineers.

   • Establish and propagate best practices, design patterns, and standards.

   • Effectively document architectural decisions, data flows, interface contracts.

9. Innovation & Continuous Learning

   • Stay up‑to‑date with SAP Datasphere roadmap, SAP Business Technology Platform (BTP), trends in cloud data platforms, data fabrics, knowledge graphs, machine learning integrations.

   • Recommend adoption of new technologies or approaches when beneficial

All your information will be kept confidential according to EEO guidelines.