Data and AI Architect

Make a Difference in YOUR Career! 

Our vision is both simple and ambitious: to put our drinks on every table. 

We are the leading global independent beverage solutions provider. We serve a broad range of national and international retailers as well as Global, National and Emerging (GNE) brands. Our products are distributed worldwide from our production sites in Europe, North America, and Australia.  Although our own branding may not appear on the labels of the beverages we produce, there is a good chance you are reading this while sipping one of our drinks. 

Our ambition is to continually improve and it’s what keeps us at the top of our game.  We are solutions-based.  We are innovative.  We seek out new challenges and conquer them.  This is our company ethos, but it’s our people’s too: Refresco is at the cutting edge of a fast-moving industry because we have passionate people pushing the boundaries of what’s best. 

Stop and think: how would YOU put our drinks on every table? 

The Data Architect is the senior technical lead for the enterprise data and AI platform at Refresco. This 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.

The Data Architect partners closely with the IT leadership and works across Business Applications, functional teams, and business stakeholders to translate complex data and AI needs into scalable, governed, and business-aligned platform capabilities. The ideal candidate combines deep SAP expertise with hands-on data engineering fluency and an active AI practitioner mindset.

Essential Functions:

• 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/4HANA, BW/4HANA, 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 LangChain, LangGraph, 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.

Education and Experience:

• 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 enviro