Delivery Solutions Architect

Details

• Engage with the Solutions Architect to understand the full Use Case Demand Plan for prioritized customers.
• Own the Post-Technical Win technical account strategy and investment plan for the majority of Databricks Use Cases within our most strategic accounts.
• Be the accountable technical leader assigned to specific Use Cases and customer(s) across multiple selling teams and internal stakeholders, creating certainty from uncertainty/ambiguity and driving onboarding, enablement, success, go-live and healthy consumption of the workloads where the customer has made the decision to consume Databricks.
• Be the first point of contact for any technical issues or questions related to production/go live status of agreed upon Use Cases within an account, oftentimes services multiple use cases within the largest and most complex organizations.
• Leverage both Shared Services 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 on the right tasks that are beyond your scope of activities or expertise.
• Create, own and execute a PoV as to how key use cases can be accelerated into production, bringing EM/PM in to prepare Professional Services proposals.
• Navigate Databricks Product and Engineering teams for New Product Innovations, Private Previews and Upgrade needs (DBR, E2 and Unity Catalog).
• Build and maintain an executive level as well as a detailed programme level success plan that covers all activities of Customer, PS, Partner, SSA, Product Specialist, SA to cover the below workstreams:
• Key use cases moving from 'win' to production
• Enablement / user growth plan
• Product adoption (strategy and activities to increase adoption of LH vision)
• Organic needs for current investment Eg. Cloud Cost control, Tuning & Optimization
• Executive and operational governance
• Proactively provide internal and external updates - KPI reporting on the status of consumption and customer health, covering investment status, key risks, product adoption and use case progression - to your Technical GM
• Development of reusable and scalable assets and mentorship of junior team members to establish the DSA team
• Key use cases moving from 'win' to production
• Enablement / user growth plan
• Product adoption (strategy and activities to increase adoption of LH vision)
• Organic needs for current investment Eg. Cloud Cost control, Tuning & Optimization
• Executive and operational governance
• 8+ years in a customer-facing pre-sales, technical architecture, customer success, or consulting role
• Experience understanding architecture related distributed data systems, specifically within one of the following:
• Data Engineering technologies (e.g. Spark, Hadoop, Kafka)
• Data Warehousing (e.g. SQL, OLTP/OLAP/DSS)
• 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
• Executive Stakeholder management - experience in effectively engaging and influencing a variety of audiences (technical, non technical) at all levels of an organization with particular success in building and maintaing strong CxO level relationship
• Executive escalation management - experience in resolving complex and critical escalation with senior customer and internal executives
• Strategic Management Consulting - experience of conducting open-ended discovery workshops, creating strategic roadmaps, conducting business analysis and managing delivery of complex programmes/projects
• Building and steering to a value case - business value consulting and realization
• Quota ownership, achievement and track record of great performance against objective target
• Proficient in both Korean and English (Native level Korean and Business level English) verbally and in writing
• Data Engineering technologies (e.g. Spark, Hadoop, Kafka)
• Data Warehousing (e.g. SQL, OLTP/OLAP/DSS)
• Data Science and Machine Learning technologies (e.g. pandas, scikit-learn, HPO)