Databricks Partner Solution Architect

Role Summary

Sutherland's Databricks practice turns the Data Intelligence Platform into business outcomes for clients. The Databricks Partner Solution Architect shapes pursuits from first conversation to signed statement of work, then makes sure the solution the client bought is the one that gets delivered. They are the technical anchor between Sutherland's sales and industry teams, Databricks field teams, and the delivery organization.

The role reports to the Head of Data Practice and works across sales, industry leads, delivery architects and Databricks partner teams. The architect's job is to make sure every proposal is technically sound, commercially defensible and achievable by the delivery team, and that the platform decisions made in the pursuit still hold once implementation starts.

Key Responsibilities

• Pursuit leadership: Qualify opportunities with sales and industry teams, define solution strategy, and lead the technical content of proposals, presentations, demos and statements of work.
• Solution architecture: Design scalable Databricks solutions across data engineering, lakehouse architecture, data warehousing, analytics, machine learning, GenAI and agentic AI workloads, with governance and security built in from the start.
• Platform advisory: Advise clients on current Databricks capabilities, including Unity Catalog, Lakeflow, Delta Lake, Apache Iceberg interoperability, Databricks SQL, AI/BI, Genie, Mosaic AI, Agent Bricks, Model Serving, Vector Search, MLflow, Databricks Apps and Lakebase, and recommend fit-for-purpose choices.
• Business case development: Build TCO, ROI, modernization and migration narratives that show executives the business impact of Databricks investments.
• Partner-led growth: Work with Databricks field teams and Sutherland market teams to identify, shape and expand opportunities across priority industries and regions.
• Reusable assets: Contribute to reference architectures, accelerators, demos and sales plays that help Sutherland scale its Databricks partnership.
• Sales-to-delivery handover: Provide technical oversight during the transition from pursuit to delivery, confirming that scope, estimates and architecture are achievable, secure, governed and aligned to client outcomes.
• Quality and governance: Review solution designs against Sutherland's standards for security, data governance, performance and production readiness before they go to clients.

Qualifications and Experience

• Senior data and AI architecture: 10+ years in consulting, data architecture, analytics, AI or cloud engineering, including 5+ years designing and delivering modern data platforms in cloud environments.
• Databricks depth: Hands-on experience with Databricks is required, with a strong understanding of lakehouse architecture, data engineering, governance, analytics and production implementation patterns.
• Presales and pursuit leadership: 3+ years leading technical presales, solutioning or pursuit processes, with a record of partnering with sales and consulting leaders on client-specific solutions.
• AI fluency: Working knowledge of GenAI, agentic AI, RAG, ML and LLMOps, model evaluation, model serving and governed AI patterns. Experience with Mosaic AI, Genie, Agent Bricks, MLflow or Vector Search is a strong advantage.
• Cloud platform knowledge: Experience with AWS, Azure or GCP data services, including storage, networking, identity and access management, databases, compute and security controls.
• Delivery credibility: Background in data modeling, pipeline development, DevOps and CI/CD, migration, performance optimization and production platform operations.
• Commercial and executive presence: Ability to translate complex technical topics into business value for executives, technical buyers and delivery teams.
• Relationship building: Track record of growing client relationships and building trust across sales, delivery and technical stakeholders.
• Industry experience: Deep experience in banking, insurance or financial services is strongly preferred.
• Certifications: Databricks certifications (such as Certified Data Engineer Professional or Certified Generative AI Engineer) and cloud architect certifications are an advantage.
• Language: Excellent English communication skills, both written and verbal.