Resultant
Partner Sales Manager - Databricks
Chicago, IL, United States
Sponsorship not specified$25k-$50kDetected 8 days ago
SnowflakeDatabricksAWSGCPAzureMachine LearningData AnalysisData EngineeringData ScienceSalesProcurement5G/LTEUnityResearchLeadership
About the role
- Resultant is a modern consulting firm with a radically different approach to solving problems.
- We don't solve problems for our clients.
- Through outcomes driven by data analytics, technology solutions, digital transformation, and beyond, our team works with clients in both the public and private sectors to solve their most complex challenges.
Responsibilities
- respond timely, stay organized, and follow through to build trust
- We trust each other to own our work and deliver exceptional results.
Nice to have
- Experience with sports/entertainment and gaming is a big plus for this role.
- Originate qualified pipeline.
- Run multi-stakeholder, 3-12-month commercial sales cycles from first conversation through security review, legal, and contract close.
- Engage Resultant's Databricks engineering and solution architects early.
- Co-sell with Databricks and the cloud ecosystem.
- Operate inside Databricks deal registration discipline.
- Conduct territory mapping and account targeting with Databricks private-sector field teams.
- Ensure Databricks private-sector sellers have current Resultant content, case studies, and contacts at their fingertips.
Compensation
- $25k-$50k
Equal opportunity
- We embrace AI across everything we do, and that includes how you prepare for this interview.
- Feel free to use AI tools to research the role, practice your responses, and put your best foot forward.
- What matters to us is getting to know the real you - how you think, how you communicate, and what you genuinely bring to the table.
- Our interviews are designed to go deeper than any polished answer, so come curious and come as yourself without the support of an AI tool during our conversation.
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This listing is sourced directly from Resultant's careers page and normalized into a canonical job model.