Databricks

Databricks

Director, Field Engineering - Financial Services

Georgia; Illinois; New York; Texas; Washington, D.C · Director

Sponsorship not specified$218k-$300kDetected 4 days ago
DatabricksSparkData EngineeringSalesCadenceLeadershipMentoring

About the role

  • Your experience partnering with the sales organization will help close revenue opportunities with the right approach whilst coaching new sales and pre-sales team members to work together.
  • The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.
  • Based on the factors above, Databricks anticipates utilizing the full width of the range.

Responsibilities

  • You will also work with various internal and external partners, and be responsible for driving strategy and execution to deliver against our audacious goals.
  • You will create a culture of psychological safety to develop and grow a team experienced in enterprise software, big data/analytics, data engineering, and data science.
  • You will guide and get involved to enhance your team's effectiveness; be an expert at communicating complex, business value-focused solutions; support complex sales cycles, and build relationships with key stakeholders in large corporations.
  • Hire and manage first-line Managers and a growing team of technical pre-sales Solutions Architects.
  • Build a collaborative culture within a rapid-growth team. Embody and promote Databricks' customer-obsessed, truth-seeking, diverse culture and a first principles mindset
  • Proven leadership ability to influence, develop, and empower your team to achieve objectives with a team approach.
  • Drive operational rigor to manage a high-growth business, with the right cadence, drive visibility and support for customer issues and feature requests.
  • Track record of building strong ecosystems of lucrative customer relationships and cross-functional partnerships (Sales, Engineering, Marketing).

Requirements

  • Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent experience through work experience.

Compensation

  • Databricks is committed to fair and equitable compensation practices.
  • The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.
  • Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location.
  • The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above.
  • Local Pay Range
  • $218,400 - $300,300 USD

Benefits

  • 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.

Company info

  • As the Director of Field Engineering, you will be responsible for partnering with our enterprise customers to democratize Data and AI within their organizations, leveraging Databricks solutions and expertise to address their most critical challenges.
  • Reporting to the Tech GM of Field Engineering for Financial Services, the Director, Field Engineering will lead a team of pre-sales Managers and Solutions Architects, focusing on acquisition of new customers.
  • Create trust-based relationships with customers for the long term and understand your industry and sub-verticals, landscapes and trends, to help influence the direction of our accounts.
  • At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel.
  • Ability to elevate engagement, with a track record of driving large transactions and high-growth customers.

Equal opportunity

  • Our Commitment to Diversity and Inclusion

This listing is sourced directly from Databricks's careers page and normalized into a canonical job model.