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Serve a critical role to empower Databricks customers to solve critical business challenges with the Databricks Lakehouse platform; help a customer accelerate their time to value on Databricks by providing technical and business guidance to build their data engineering and data science use cases on Databricks and collaborate with the company’s field engineering teams to build a technical strategy for select accounts to drive innovation, execution, and growth. Detailed job duties include: Own the Post-Technical Win technical account strategy 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), driving onboarding, enablement, success, go-live and healthy consumption of the workloads where the customer has made the decision to consume Databricks. Help accounts in architecting, building, and testing both data engineering and machine learning workloads, leveraging technologies such as Delta Lake, MLflow, DB SQL, and more. Develop cloud strategies that align with the client's goals and objectives by advising appropriate tools and technologies needed to implement said workloads. Develop mutual success plan that covers all activities of all customer-facing technical roles and teams to cover the below work streams: Main use cases moving from ‘win’ to production; Product adoption (strategy and activities to increase adoption of Databricks’ Lakehouse vision); Enablement / user growth plan; Organic needs for current investment Eg. Cloud Cost control, Tuning & Optimization; Executive and operational governance; Serve as the first 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. Derive insights by analyzing large, complex data sets to identify trends and patterns using Databricks SQL. Design and develop visualizations and dashboards to effectively communicate data insights and KPIs to both internal and external account stakeholders, including the Technical GM. Create, own and execute a point-of-view 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. $182,187 - $195,000 per year. Minimum job requirements: Master’s degree in Computer Science, Data Science, Information Systems Management, Business Analytics or related field and at least three years of experience. Will accept Bachelor’s degree plus five years of experience in lieu of Master’s degree plus three years of experience. Must have experience in the following areas: Programming experience with at least one of the following: Python, SQL or Scala; Experience with architecture related distributed data systems, specifically within one of the following: 1) Data Engineering technologies including at least one of the following: Spark, Hadoop or Kafka; 2) Data Warehousing including at least one of the following: SQL, OLTP, OLAP or DSS; or 3) Data Science and Machine Learning technologies including at least one of the following: pandas, scikit-learn or HPO; Experience in designing cloud-based data ingestion and data integration (ETL/ELT) pipeline; Experience in designing solutions on one of the following cloud infrastructure and services: AWS, Azure or GCP; May telecommute from any US location.