SAP Data Platform Architect
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Key skills for this role
About the Role
**SAP Data Platform Architect is required by our global client for new SAP S/4 HANA project** **The successful candidate will be responsible for** * Review business requirements for the integration of new data sources , especially AI\-related cases * Propose design improvements, identify solution options, assess feasibility and impact, and develop recommendations * Align with enterprise architect, and prepare documentation for approval process * Guide and oversee
Key Skills for This Role
Full Job Posting
Overview
- SAP Data Platform Architect is required by our global client for new SAP S/4 HANA project
- The successful candidate will be responsible for
- Review business requirements for the integration of new data sources , especially AI-related cases
- Propose design improvements, identify solution options, assess feasibility and impact, and develop recommendations
- Align with enterprise architect, and prepare documentation for approval process
- Guide and oversee platform configuration, data model setup and metadata/ catalogue
- integration.
- Collaborate with vendors, manage deliverables, and ensure architectural alignment.
- Project management support, e.g.,tracking timelines, scope and deliverables from vendor
- Essential previous experience must include
- Strong experience with designing and implementing enterprise data platforms (e.g., Cloudera, Azure, Databricks etc.) and key components of such platforms, e.g., Dataiku
- Hands-on knowledge of data lake house patterns, including batch/real-time ingestion,ETL/ELT, and data modeling(dimensional, semantic).
- 8 years in Data Architecture Engineering
- 2+ full implementations of data platforms
- Background in working with SAP systems for data
- Experience working with open- source or hybrid tech stacks (e.g., Trino, Iceberg, DBT,
- Airflow, Prometheus).
- Knowledge of governance and metadata management tools (e.g., Apache Atlas, OpenMetadata).
- Familiarity with AI/ML workbench integration, data science workflows, and data versioning.
- Good understanding of containers (Kubernetes) and orchestration tools.
- Strong communication, documentation, and stakeholder mgmt. skills.
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