Solutions Architect

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