Data and AI Architect

The Data Architect is the senior technical lead for the enterprise data and AI platform s role designs, builds, and governs a modern open data ecosystem anchored on SAP Business Data Cloud (BDC) while architecting AI‑powered solutions using SAP BTP, SAP AI Core, HANA Cloud, and GenAI/LLM platforms.

• Own and evolve the enterprise data architecture centered on SAP BDC, Datasphere, and BTP — including Medallion architecture, Data Products, Delta Lake, and zero‑copy sharing — ensuring alignment with Business Applications strategy and clean‑core principles.
• Design cross‑platform integrations across SAP S/4
  
  HANA, BW/4
  
  HANA, non‑SAP systems (IoT, SaaS, OT data), and hyperscalers (Azure, AWS, GCP) to enable a unified, governed data fabric.
• Build and maintain enterprise‑grade data pipelines using Python, SQL, dbt, Airflow, and Spark alongside SAP‑native tools (ODP/CDS extraction, Datasphere Data Integration), with CI/CD practices and pipeline observability standards.
• Optimize SAP HANA Cloud for real‑time analytics and AI workloads — including advanced SQLScript, vector embeddings, and Knowledge Graphs — to support both operational and analytical consumption patterns.
• Architect and deploy GenAI and ML solutions using SAP AI Core, SAP AI Launchpad, and LLM platforms (Azure OpenAI, AWS Bedrock, SAP GenAI Hub), integrating them seamlessly into SAP business processes.
• Design agentic AI workflows that automate decision‑making by combining LLMs, structured SAP data, and enterprise APIs — using frameworks including Lang Chain, Lang Graph, A2A, and MCP.
• Build RAG and Graph RAG applications using HANA Cloud Vector Engine and SAP Knowledge Graphs to ground AI outputs in trusted enterprise data.
• Identify and prioritize high‑value AI/ML use cases across business processes and lead delivery from prototype to production in collaboration with business and IT teams.
• Establish and enforce data governance frameworks covering data ownership, lineage, metadata management, data quality, and access controls using catalog tooling (SAP Metadata Explorer, Collibra, Alation, or equivalent).
• Define and operationalize Responsible AI practices — including model risk, explainability, bias detection, and ethical AI compliance — aligned with enterprise policy and regulatory requirements.
• Enable governed self‑service analytics across SAP BDC, SAC, Datasphere, and Power BI, including semantic modeling, row‑level security, and role‑based access.
• Lead high‑complexity data and AI projects end‑to‑end, including stakeholder alignment, scope management, and status reporting to leadership.
• Mentor junior data team members on architecture patterns, engineering standards, and AI best practices; maintain technical decision logs and platform documentation.

• Bachelor’s degree in computer science, Information Systems, Data Science, Engineering, or related field required;
  Master’s degree preferred.
• 15+ years of experience in data architecture, data engineering, or BI/analytics roles in enterprise environments.
• Minimum 3 years of deep, hands‑on SAP experience — BDC, Datasphere, BW/4
  
  HANA, and S/4
  
  HANA analytics.
• Demonstrated experience delivering GenAI or agentic AI solutions in enterprise environments, from prototyping to production.
• Demonstrated experience building production data pipelines using Python and SQL alongside SAP‑native tooling.
• Experience in manufacturing, CPG, or similarly complex operational environments is a strong differentiator.
• SAP BDC, BTP AI Core, or Datasphere certifications are advantageous; cloud AI certifications (Azure AI, AWS ML) and TOGAF are a plus.

• SAP Platform:
  Hands‑on expertise with SAP BDC, Datasphere, BTP (AI Core, Integration Suite, Extension Suite), HANA Cloud (including vector processing and Knowledge Graphs), BW/4
  
  HANA, SAC, and S/4
  
  HANA CDS/AMDP.
• AI & GenAI:
  Proficiency with Lang Chain, Lang Graph, RAG/Graph RAG, vector databases, LLM platforms (Azure OpenAI, AWS Bedrock, SAP GenAI Hub), A2A/MCP frameworks, prompt engineering, and SAP Business AI / Joule.
• Data Engineering:
  Strong command of Python (Pandas, PySpark, SQL Alchemy), SQL, dbt, Apache Airflow/Spark, Git/CI‑CD,…