Sr. Delivery Solutions Architect

CSQ424R249

Mission:

At Databricks, we are on a mission to empower our customers to solve the world's toughest data problems by utilizing the Data Intelligence platform. As a Sr. Delivery Solutions Architect (DSA), you will be critical during this journey. You will collaborate with our sales and field engineering teams to accelerate the adoption and growth of the Databricks platform in your accounts. As a Sr. DSA, you will help ensure customer success by driving focus and technical accountability to our most complex customers who need guidance to accelerate consumption on Databricks workloads they have already selected.

This is a hybrid technical and commercial role. It is commercial in that you will be required to own and drive growth in your assigned customers and use cases through leading your customers’ stakeholders, owning executive relationships, and creating and driving plans and strategies for Databricks colleagues to execute. This is parallel to being technical, with expectations being that you become at least Level 200 across all Databricks products/workloads and the use case-specific technical lead post-Technical Win. This requires you to utilize relationship management skills and technical credibility to engage and communicate at all levels with an organization effectively. You will report directly to a DSA manager of the Manufacturing Business Unit. 

Your day-to-day responsibilities:

• Engage with your Account team to understand the full use case for customers
• Be the first point of contact for any technical issues or questions related to the production/go-live status of agreed-upon use cases within an account, often servicing multiple use cases within the largest and most complex organizations. 
• Own the Post-Technical Win technical account strategy and execution plan for most Databricks use cases within our most strategic accounts.
• Be the technical leader assigned to specific use cases and customers across multiple business units and project stakeholders.
• Create certainty from uncertainty/ambiguity, and drive the onboarding, enablement, success, go-live, and healthy consumption for customer workloads 
• Leverage Shared Field Engineering team of User Education, Onboarding/Technical Services, and Support resources, along with escalating to Level 400/500 technical experts (Specialist Solution Architects and Product Specialists) to execute the right tasks beyond your scope of activities or expertise. 
• Create, own, and execute a Point of View on how critical use cases can be accelerated into production, bringing the Engagement Manager/Project Manager in to prepare Professional Services (PS) proposals.
• Navigate Databricks Product and Engineering teams for New Product Innovations, Private Previews, and Upgrade needs (DBR, E2, and Unity Catalog). 
• Build and maintain a technical delivery plan that covers all activities of the Customer, PS, Partner, SSA, Product Specialist, and SA to cover the below workstreams:
• Critical use cases moving from ‘win’ to production
• Enablement/user growth plan
• Product adoption (strategy and activities to increase adoption of the Data Intelligence Platform)
• Organic needs for current investment, Cloud Cost control, Tuning, and optimization
• Executive and operational governance (for example - Quarterly Business reviews)
• Proactively provide internal and external updates - KPI reporting on the status of consumption and customer health, Learning and Enablement status, key risks, product adoption, and use case progression.

What we look for (Competencies):

• 9+ years in a customer-facing pre-sales, technical architecture, customer success, or consulting role
• Technical project Delivery or Technical Project management experience within the domain of Data and AI projects
• Experience in one of the major public cloud platforms AWS/Azure/GCP (Cloud Modernization and Cloud Architecture experience is preferred)
• Experience in leading architecture-related discussions on distributed data systems, specifically within one of the following:
• Data Engineering technologies (e.g. ETL, Spark, Hadoop, Kafka)
• Data Warehousing (e.g. SQL, OLTP/OLAP/DSS, Redshift. Big Query, or similar Cloud Data Warehouses)
• Data Science and Machine Learning technologies (e.g. pandas, scikit-learn, HPO)
• Comfortable managing multiple projects at once and engaging a virtual team of subject matter experts to address any onboarding or technical challenges outside of your remit or bandwidth.
• Influencing and leading teams - especially without having direct reporting line responsibility for individuals within account and leadership teams, both internally and externally