Sr. Manager, Field Engineering
- Status
- Open
- Remote policy
- Not stated
- Employment type
- Not stated
- Salary
- Not stated
- Categories
- Field Engineering - Other
- Source
- databricks
- First observed
- 2026-08-25 02:15 UTC
- Last seen
- 2026-08-25 02:15 UTC
- Source claims posted
- 2026-08-25 00:03 UTC
- Consecutive misses
- 0 of 3
What the posting says
FEQ427R422
As a Sr.Manager, Field Engineering, you will build and lead a team of pre-sales Solutions Architects focusing on your assigned accounts for the Korean market. Your experience partnering with the sales organization will help close revenue with the right approach whilst coaching new sales and pre-sales team members to work together. 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 customers' companies. You will report to the Director, Field Engineering based in Korea.
The impact you will have:
Manage hiring, building the pre-sales team of Solutions Architects
Rapidly scale the designated Field Engineering segment organization without sacrificing quality
Build a collaborative culture within a rapid-growth team. To embody and promote Databricks' customer-obsessed, teamwork, and diverse culture
Support increase Return on investment of SA involvement in sales cycles by 2-3x over 18 months
Promote a solution and value-based selling field-engineering organization
Display an understanding of business needs and revenue potential for accounts in the assigned region
Build Databricks' brand in partnership with the Marketing and Sales team
What we look for:
Experience in a Pre-Sales Manager role in developing, managing and building a team of successful Big Data, Cloud, or SaaS professionals
Have experience scaling and mentoring field and technical teams from scratch both onshore and offshore teams.
Knowledgeable in and passionate about data-driven decisions, AI, and Cloud software models
Great at instituting processes for technical field members to improve efficiency
Background experience in Data Architecture such as Data Lake, Data Engineering / Data Warehouse technologies or Data Science
Native level Korean required; business‑level English strongly preferred
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.
Quality
- x Salary range stated weight 35%
- x Remote policy stated weight 20%
- + Location stated weight 15%
- + Organisation stated weight 15%
- + Publication date stated weight 15%
Not enough history yet to judge honesty signals.
Timeline
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#345128 2026-08-25 02:15 UTCPublished