Databricks
Solutions Architect - Financial Services (Hunter)
Georgia; Illinois; Massachusetts; New Jersey; New York; Washington, D.C
Sponsorship not specified$180k-$248kDetected 4 days ago
PythonJavaScalaSQLDatabricksAWSGCPAzureCloud PlatformsKafkaMachine LearningSparkData AnalysisData EngineeringData ScienceSalesResearchLeadershipHadoop
About the role
- As a team, we have expertise in cloud platforms, data engineering, data analytics, and data science and machine learning.
- You will report to the Field Engineering Manager for the team.
- 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.
Requirements
- [Desired] Degree in a quantitative discipline (Computer Science, Applied Mathematics, Operations Research)
Skills
- Have built solutions with public cloud providers such as AWS, Azure, or GCP
- Spark, Hadoop, Kafka )
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
- $180,000 - $247,500 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
- At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel.
Equal opportunity
- Our Commitment to Diversity and Inclusion
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This listing is sourced directly from Databricks's careers page and normalized into a canonical job model.