Pre-sales Engineering Manager (Retail & CPG)

Databricks
📍 London, United Kingdom
22h

(FEQ427R369)

As a Manager, Field Engineering (Data and AI), you will lead a team of pre-sales Solutions Architects focusing on complex, strategic accounts, helping to lead our UK&I expansion with a specific focus on the Retail and Consumer Packaged Goods sectors. Your experience partnering with sales organisations will help to drive accelerated adoption of the platform in customers, demonstrated by revenue growth, while coaching new sales and pre-sales team members to work together. You will guide your team and be involved with opportunities to enhance your team's effectiveness. You must be a skilled communicator with experience addressing complex, business value-focused solutions; supporting complex sales cycles; and building relationships with key stakeholders in large corporations. 

You will report to the Director, Field Engineering.

 

The impact you will have:

What we look for:

 

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About Databricks

Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.

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At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.

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At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.

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