Solutions Architect - CPG and Manufacturing

About the role

As a Solutions Architect you will shape the technical strategy for Databricks customers, own architecture discussions and drive platform adoption. You will act as a trusted technical partner to customer architects and engineering leads, combining deep expertise with a hands‑on builder mindset to position Databricks as the foundation of their data and AI initiatives.

Key responsibilities

• Own end‑to‑end technical strategy for accounts, from discovery through production deployment.
• Lead complex architecture discussions designing production‑grade data engineering, AI/ML, agentic systems and real‑time analytics solutions.
• Serve as a trusted technical partner to customer architects, engineering leads and directors, shaping their data and AI journey.
• Develop an emerging technical specialization (e.g., AI/ML, agentic architectures) and become a go‑to resource within the team.
• Orchestrate cross‑functional resources (DSAs, SSAs, partners) to deliver end‑to‑end AI and data solutions.
• Provide structured feedback to product teams on customer requirements, AI capabilities and competitive gaps.

Required profile

• 6+ years of experience in solutions architecture, data engineering, data science/AI or technical pre‑sales with a strong record of hands‑on solution building.
• Deep expertise in modern data and AI architectures, including lakehouse design, scalable pipelines, real‑time/streaming and cloud‑native platforms.
• Strong understanding of AI/ML concepts, agentic architectures, ontology design, knowledge graphs and semantic data models.
• Proficiency with the Databricks platform and experience deploying production workloads on public clouds (AWS, Azure or GCP) with security and governance considerations.
• Track record of driving platform adoption and consumption growth for AI/ML and analytics workloads.
• Bachelor’s or Master’s degree in Computer Science, Engineering or a quantitative discipline.

Required skills

• Databricks platform
• Lakehouse architecture
• Scalable data pipelines
• Real‑time streaming
• Cloud‑native platforms (AWS, Azure, GCP)
• AI/ML
• Agentic architectures
• Ontology design, knowledge graphs, semantic data models
• Security and governance on public cloud