Solutions Architect – Data & AI (Presales) at Databricks As a Solutions Architect, you will combine customer engagement with hands-on technical expertise. You will work closely with customers to understand their challenges and demonstrate how the Databricks platform can solve them. Responsibilities Partner with Account Executives to drive technical sales cycles and customer outcomes Lead customer workshops, discovery sessions, and technical deep-dives across multiple stakeholders Build and deliver proof-of-concepts, demos, and solution prototypes using real data Translate business requirements into practical, scalable data and AI solutions Act as a trusted advisor, while also being able to go hands-on to validate solutions end-to-end Work through technical challenges with customers, iterating and resolving issues as they arise Contribute reusable assets, patterns, and best practices that accelerate adoption across customers Requirements Customer-facing mindset Experience working with customers in a presales, consulting, or advisory capacity Ability to communicate complex topics to both technical and non-technical audiences Comfortable leading discussions that connect business goals to technical decisions Hands-on technical foundation Experience with Python (or similar) and familiarity with distributed data processing and compute and working with large-scale datasets Understanding of data pipelines and modern data architectures Ability to work practically with data (e.g. building PoCs, exploring datasets, troubleshooting) Comfortable reasoning through code, explaining how it works, and adapting it when things don’t behave as expected Data & AI understanding Familiarity with analytics, data science, or machine learning workflows Understanding of how data platforms support AI use cases (e.g. feature pipelines, model training, inference) Interest in modern AI capabilities, combined with an understanding of how they operate on real data systems Architecture & platform understanding Familiarity with cloud platforms and data technologies Ability to design solutions that consider scalability, performance, reliability, and data volumes Awareness of trade-offs in architecture decisions and ability to adapt designs based on constraints Success factors Comfortable switching between business dialogue and technical execution Building, validating, and iterating on solutions in practice Taking ownership of technical challenges and pushing problems forward Applying structured thinking in discovery and connecting requirements to scalable technical solutions Understanding that AI solutions depend on solid data foundations Curious and continuously deepening technical expertise