Sr. Manager, Field Engineering

Databricks
United StatesPosted 1 April 2026

Job Description

FEQ427R181 The Databricks Field Engineering team activates and accelerates the value our customers get from their data and AI. Major Enterprises in the Retail, Consumer Products, and Travel Hospitality industry are leveraging the Databricks Data Intelligence Platform to accelerate their data-driven strategies. Our team is hiring a dynamic Solution Architecture (SA) leader who intimately understands the large enterprise ecosystem and how a modern data architecture is critical to advancing their Data AI initiatives. Additionally, this SA leader will inspire their team of Solution Architects by translating their team's vision into a tangible and effective strategy in order to drive our customers’ data and AI experiences forward. The Sr. Manager, Solutions Architecture, will help lead a team of Hunter SAs within Databricks’ Field Engineering team's Retail vertical. Reporting to the Technical GM - Retail, Field Engineering, you will lead and promote a dynamic team focusing on enterprise software, big data/analytics, data engineering, and data science. Leading the technical split-focused team (pre-sales, technical acumen, demonstrations), you will partner with Sales (and other Field Engineering technical segments) to increase revenue and help customers become wildly successful. You'll scale and maintain an outstanding Field Engineering team that operates efficiently to accelerate Databricks' market growth. The Impact You Will Have You will hire, train, grow, and manage a team of Solutions Architects for a company in high-growth mode. Make your customers in the Media Publisher segment successful with Databricks and provide outsized value to their businesses. You will maintain a robust hiring pipeline at all times. Establish relationships across the business to make your customers and team successful. Partner with sales leadership to hit sales and consumption targets and ensure customer success Keep your team of SAs ahead of the technical curve. An SA adds value by maintaining advanced knowledge of the technology stack, while tying it back to key initiatives and trends across this industry. You will make sure that your team is continuously learning and working to provide our customers with the most comprehensive solutions for their needs What We Look For 7+ years of professional experience in the data space with a technical product (i.e. data warehousing, big data, or machine learning). 3+ years of professional experience in the field, architecting and delivering data-driven solutions for major accounts within the Retail, Consumer Products, Travel Hospitality vertical. Deep familiarity with the buy-side and supply-side ecosystem Demonstrated expertise with data collaboration ecosystem (eg. customer data platforms, clean rooms, data marketplaces). 3+ years of experience building and leading technical pre-sales teams - hiring, on-boarding, and enabling pre(and post)-sales professionals A deep technical understanding of the impact that Data AI can drive within the Retail industry. Trusted advisor to technical executives who guide strategic data infrastructure decisions. Lead a team through best practices for technical qualification, proof of concepts, architecture discussions, and product demonstrations. Experience hiring candidates, ramping them up to be successful, and promoting them into larger roles. Build team morale and foster working relationships among Field Engineering, Sales, and other key internal partners. Experience working with teams across Sales, Product Management, Engineering, and Customer Success. Must be technical enough to earn the trust of Engineering talent and leadership at Databricks. Demonstrated architectural influence. Able to influence and review complex architectures, guiding your team and customers toward ideal solutions - that scale. Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represe ... (truncated, view full listing at source)
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