> Markdown version of [/jobs/ext/177953-delivery-solutions-architect](https://www.wearedevelopers.com/jobs/ext/177953-delivery-solutions-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Delivery Solutions Architect - **Company:** Databricks - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Information Systems, Distributed Data Store, Python (Programming Language), Apache Spark, Information Technology, Databricks - **Published:** May 22, 2026 - **Apply:** https://www.nettemps.com/us/en/W9C8D9B901541856989.ntap ## About the Role * 5+ years of experience delivering technical projects/programs in Data and AI, contributing to technical debates and design choices with customers * Experience programming in Python or Spark * Background in a customer-facing pre-sales, technical architecture, customer success, or consulting role * Understanding of solution architecture for distributed data systems * Ability to attribute business value and outcomes to specific project deliverables * Experience in technical program or project management, including account, stakeholder and resource management * Track record of handling complex, high-level escalations with senior customer executives * Experience delivering open-ended discovery workshops, strategic roadmaps, business analysis and complex program delivery * Track record of overachievement against quota or similar objective targets * Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent through work experience * Willingness to travel up to 30% when needed ## Description At Databricks, we are on a mission to empower our customers to solve the world's toughest data problems by utilizing the Databricks Data Intelligence Platform. As a Delivery Solutions Architect (DSA), you will collaborate with sales and field engineering teams to accelerate adoption and growth of the Databricks platform. You will also help ensure customer success by providing technical accountability for complex customers. Responsibilities * Engage with Solutions Architects to understand the full use case demand plan for prioritised customers * Lead the post-technical win technical account strategy and execution plan for majority of Databricks use cases within strategic accounts * Serve as the accountable technical leader for specific use cases and customers across multiple selling teams and internal stakeholders, driving onboarding, enablement, success, go-live, and healthy consumption * Act as first contact for any technical issues or questions related to production/go-live status of agreed use cases within an account * Leverage Shared Services, User Education, Onboarding/Technical Services and Support resources, escalating to expert-level technical experts as needed * Create, own and execute a point-of-view on how key use cases can be accelerated into production, coordinating with Professional Services on delivery of PS Engagement proposals * Navigate Databricks Product and Engineering teams for new product innovations, private previews and upgrade needs * Develop an execution plan covering all activities of customer-facing technical roles to include: + Main use cases moving from win' to production + Enablement/user growth plan + Product adoption strategy to increase adoption of the Lakehouse vision + Optimisation of current investment (cloud cost control, tuning) + Executive and operational governance * Provide internal and external updates - KPI reporting on usage, customer health, product adoption, and use case progression - to the Technical GM ## Related Videos - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Microservices? Monoliths? An Annoying Discussion!](https://www.wearedevelopers.com/videos/970-microservices-monoliths-an-annoying-discussion) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) ## Related Articles - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)