Practice Customer Engineer, Data Analytics, Startups, Google Cloud

Overview As a Practice Customer Engineer focused on Cloud AI, you partner with technical sales to differentiate Google Cloud for data analytics workloads. You drive technical wins by prototyping and delivering customer-tailored solutions that meet executives’ and engineers’ needs. You translate engagements into reusable assets and provide actionable feedback to product and engineering. You collaborate with GTM and engineering to stay ahead of trends and accelerate adoption of advanced AI and data analytics on Google Cloud. Compensation / Benefitsbonus targetequitybenefitsworld-class AI portfoliocollaborative culture ResponsibilitiesDrive technical wins for complex data analytics workloads across the sales cycle, from use case to customer rampProvide deep technical consultation and build lasting relationships with customer domain expertsPrototype and develop solutions to demonstrate value and secure buy-inLeverage customer learnings to create reusable assets with the Go-To-Market teamProvide feedback to Product and Engineering to improve architectures and solutionsDocument and prioritize customer feature requests and issues within management systemsCollaborate with product marketing and engineering to stay aligned with industry trends and enhance Google Cloud offerings Key requirementsBachelor's degree or equivalent practical experience10 years of cloud-native architecture experience in a customer-facing or support roleExperience with Big Data technologies (analytics warehousing, data processing/transformation, data governance, ETL/ELT, SQL, Spark) and performance/scalability optimizationExperience engaging with technical stakeholders or executive leadersExperience using Python, Spark, or SQL to demo, prototype, or workshop with customersExcellent communication and presentation skillsAbility to engage with diverse stakeholdersConsultative problem-solving approachCloud-native architectureBig Data technologies (ETL/ELT, data warehousing, SQL, Spark)Data governance and data migrations