- Location
- Atlanta
- Workplace
- Remote, Hybrid
- Type
- Full-time
- Department
- IT
- Closing date
- Apr 18, 2026
- Source
- CareersPage
Description
SAP DataSphere Consultant
Location: Remote
Duration: Fulltime
Salary: $150K - $170K / Year + Benefits
Job Description:
We are seeking an experienced SAP Datasphere Consultant to design, develop, and optimize modern data architecture and analytics solutions using SAP Datasphere. The ideal candidate will have strong hands-on expertise in data modeling, data integration, semantic modeling, governance, and cloud analytics. This role involves working closely with business stakeholders, data engineers, and analytics teams to deliver end to end data products and governed data layers
Roles & Responsibilities:
1. Solution Design & Architecture:
Design scalable data models, semantic layers, and analytic models using SAP Datasphere.
Architect hybrid scenarios integrating Datasphere with SAP and non-SAP systems.
Define data products, shared entities, and governed data layers across business domains.
Create optimal strategies for federation vs replication based on business and performance needs.
2. Data Integration & Modeling:
Build and maintain Data Flows, Replication Flows, and pipeline orchestration.
Integrate data from S/4HANA, SAP BW/4HANA, SAP ECC, cloud applications, and 3rd party sources.
Develop harmonized and curated layers, including SCD handling, data quality rules, and transformations.
Work with Business Builder to create business entities, relationships, measures, and semantic models.
3. Analytics & Consumption Layer:
Design Analytic Models for consumption by SAP Analytics Cloud (SAC), BI tools, and APIs.
Create KPIs, hierarchies, measures, currency conversions, and shared dimensions.
Optimize query performance through pruning, materialization, and source pushdown.
4. Governance, Security & Operations:
Implement role-based access, row-level security (RLS), and data privacy controls.
Manage transports, deployments, and versioning across development, QA, and production.
Use catalog, metadata management, and lineage views for governance.
Monitor pipeline performance, troubleshoot failures, and optimize resource usage in Spaces.
5. Stakeholder Collaboration:
Gather business requirements and translate them into scalable data architectures.
Collaborate with business teams, BI teams, and data analysts.
Provide documentation, technical guidance, and user training.