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Databricks

Manager, Field Engineering - Financial Services Industry

Toronto, Canada

The employer does not state the work arrangement.

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PayNot listed
Work arrangementNot stated
Who can applyNot stated by the employer
LevelManager
TypeNot stated
Occupationfoundry manager
Languageen
Posted2 June 2026
Last verified4 hours ago
StatusActive

About the role

REQ ID - FEQ227R90

Location: Toronto, ON

The Databricks Field Engineering team activates and accelerates the value our customers get from their data and AI. Major Enterprises in Canada are leveraging the Databricks Data Intelligence Platform to securely deliver AI’s potential to every audience, at scale. Our Field Engineering team is hiring a dynamic Solution Architecture leader who is not only focused on team culture but also can translate their team vision into a tangible and effective strategy in order to drive our customers’ data and AI experiences forward.

Reporting to the Director of Field Engineering, the Manager, Field Engineering will help lead a team of SAs for the Commercial and Enterprise segment of Databricks’ Field Engineering team focused on the Financial Services Industry. 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 (70% pre-sales, 30% 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 is efficient in its operations to help accelerate Databricks' growth in the market.

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 extremely 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 a deep understanding of the technology stack. 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:

  • 3+ years of experience building and leading technical pre-sales teams - hiring, onboarding, and enabling pre- and post-sales professionals
  • 7+ years of experience in the data space with a technical product (i.e. data warehousing, big data, or machine learning).
  • A deep technical understanding of the impact which Data & AI can drive within various industry segments
  • Trusted advisor to technical executives that 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 into larger roles.
  • Create a positive morale for the team and help foster a working relationship between Field Engineering, Sales, and other important internal partners.
  • Experience working with other teams such as Sales, Product Management, Engineering, and Customer Success.
  • Must be technical enough to earn the trust of Engineering talent and leadership Databricks.
  • Demonstrated architectural influence. Able to influence and review complex architectures, guiding your team and customers towards ideal solutions that scale.



Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range 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. Based on the factors above, Databricks anticpates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above.

Canada Pay Range$178,100—$244,850 CAD

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.

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.

Our Commitment to Diversity and Inclusion

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.

Compliance

If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

Listed via Databricks · first seen 22 October 2026 · last verified 28 October 2026

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