We are seeking an experienced SAP BDC & Databricks Consultant to support an enterprise SAP Business Data Cloud (BDC) Proof of Concept (PoC).
The ideal candidate will have strong hands-on implementation experience with SAP Business Data Cloud, SAP Databricks, and SAP Datasphere, combined with expertise in AI/ML, data engineering, data integration, and solution architecture.
The candidate will be responsible for guiding architecture decisions, designing AI/ML use cases, establishing implementation best practices, and serving as the technical point of reference during the PoC.
The role requires a strong combination of SAP data platform expertise + Databricks AI/ML + customer-facing technical leadership.
Critical Mandatory Skills
Candidates must have hands-on experience with:
- SAP Business Data Cloud (BDC)
- SAP Databricks
- SAP Datasphere
- Databricks AI/ML
- Python / PySpark / Spark SQL
- MLflow
- Data Engineering
- AI/ML Model Lifecycle Management
Candidates should also have experience integrating SAP and non-SAP data sources.
Key Responsibilities
SAP BDC & Datasphere
- Design and implement solutions using SAP Business Data Cloud (BDC).
- Work with SAP BDC and Datasphere data products to prepare data for AI/ML workloads.
- Define architecture and implementation patterns for SAP BDC PoCs and enterprise implementations.
- Support data integration between SAP and non-SAP systems.
- Develop scalable data architectures aligned with enterprise standards.
SAP Datasphere
Strong working knowledge of:
- Data Modeling
- Replication Flows
- Transformation Flows
- Data Products
- BDC Connect
- Delta Sharing
- Design and optimize data models for analytics and AI/ML workloads.
- Support data ingestion, transformation, and preparation activities.
- Ensure data is appropriately structured for downstream Databricks processing.
Databricks Architecture & AI/ML
- Design and implement AI/ML use cases using Databricks.
- Lead:
- Model Development
- Experimentation
- Model Validation
- Model Deployment
- Provide technical guidance on Databricks architecture.
- Optimize Databricks solutions for:
- Performance
- Scalability
- Governance
- Reliability
- Design scalable data pipelines supporting machine learning workloads.
- Establish best practices for data engineering and AI/ML productionization.
Databricks AI/ML Skills
Strong hands-on experience with:
- Python
- PySpark
- Spark SQL
- MLflow
- Databricks
- Delta Lake
- Feature Engineering
- Model Training
- Model Evaluation
- Model Tuning
- Model Deployment
Machine Learning & Analytics
Experience with:
- Predictive Analytics
- Time-Series Forecasting
- Feature Engineering
- Model Development
- Model Training
- Model Evaluation
- Model Optimization
- Model Deployment
- ML Lifecycle Management
- Design practical AI/ML solutions based on business requirements.
- Evaluate appropriate machine learning approaches for enterprise use cases.
- Support the transition of AI/ML models from experimentation into production.
Data Engineering
- Build scalable data pipelines for AI/ML workloads.
- Design data ingestion and transformation processes.
- Work with structured and enterprise data sources.
- Integrate SAP and non-SAP data platforms.
- Optimize data processing and pipeline performance.
- Implement appropriate data governance and security practices.
- Work with Delta Lake and Databricks data engineering capabilities.
SAP & Enterprise Data Integration
Experience integrating SAP and non-SAP data sources, including:
- SAP S/4HANA
- SAP Datasphere
- SAP Business Data Cloud
- SAP Analytics Cloud (SAC)
- SAP CDS Views
- Finance Analytics
- FP&A Data
- Design integration patterns between SAP platforms and Databricks.
- Support enterprise data flows for analytics and AI/ML workloads.
- Understand SAP business data and its transformation into analytical/ML-ready datasets.
Finance & Analytics Use Cases
Experience with one or more of the following is preferred:
- Finance Analytics
- FP&A
- Forecasting
- Predictive Analytics
- SAP S/4HANA Finance
- SAP CDS Views
- SAP Analytics Cloud
- Translate finance and business requirements into practical AI/ML solutions.
- Support forecasting and predictive analytics use cases.
- Collaborate with Finance and SAP teams to identify high-value AI/ML opportunities.
PoC Leadership
- Lead the technical implementation of the SAP BDC Proof of Concept.
- Independently drive technical architecture and implementation decisions.
- Define recommended implementation patterns for future enterprise scale-up.
- Identify technical risks, dependencies, and architecture considerations.
- Demonstrate AI/ML capabilities to business and technical stakeholders.
- Mentor customer teams on Databricks, AI/ML, and data engineering best practices.
- Serve as the primary technical point of reference for AI/ML during the PoC.
Customer-Facing Technical Leadership
- Work directly with customer SAP, BDC, Finance, Data, and Technology teams.
- Translate business problems into practical technical solutions.
- Lead technical discussions and architecture workshops.
- Present solution options, recommendations, and trade-offs.
- Provide technical guidance throughout the PoC.
- Mentor customer resources and promote best practices.
- Clearly communicate complex AI/ML and data architecture concepts to technical and business audiences.
Required Qualifications
- 6–8 years of relevant experience.
- Strong hands-on SAP Business Data Cloud (BDC) implementation experience.
- Strong hands-on SAP Databricks implementation experience.
- Good working knowledge of SAP Datasphere.
- Strong Databricks AI/ML experience.
- Expert-level Python / PySpark / Spark SQL.
- Hands-on MLflow experience.
- Strong understanding of ML model lifecycle management.
- Experience with predictive analytics and/or time-series forecasting.
- Strong data engineering and pipeline development experience.
- Strong understanding of Databricks architecture and Delta Lake.
- Experience integrating SAP and non-SAP data sources.
- Customer-facing solution architecture / technical leadership experience.
- Ability to independently lead a technical PoC.
- Strong communication and stakeholder management skills.
Preferred Qualifications
- Experience with SAP Analytics Cloud (SAC).
- Experience with SAP S/4HANA.
- Experience with SAP CDS Views.
- Finance / FP&A analytics experience.
- Forecasting or predictive analytics experience.
- Experience with enterprise SAP BDC implementations.
- Experience with large-scale Databricks implementations.
- Experience mentoring customer teams.
- Experience transitioning PoCs into production implementations.
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