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# Business Intelligence Analyst

- **Company:** Spartan Capital Inc
- **Location:** Hazlet, NJ, United States
- **Experience:** Experienced
- **Salary:** $90,000.0 - $125,000.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Data Warehousing, Python (Programming Language), Standard Sql, Tableau (Software), Information Technology
- **Published:** August 24, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=fb076a82cd96e242

## About the Role

* Minimum of 2 years of experience in BI, analytics, or reporting
 * Strong SQL
 * Hands-on Tableau experience or other dashboarding tools
 * The instinct to double-check a number that looks wrong instead of publishing it
 * Outstanding communication skills with both technical and non-technical colleagues
 * BS in computer science, engineering, mathematics, physics, or similar quantitative field, * Experience in merchant cash advance, small business lending, specialty finance, fintech, or commercial banking
 * Familiarity with factor rates, ACH returns, delinquency bucketing, cohort and vintage analysis, or default and cure rates
 * Experienced in data warehouse development
 * Python for automation, * Dependable -- more reliable than spontaneous
 * Detail-oriented -- would rather focus on the details of work than the bigger picture
 * Autonomous/Independent -- enjoys working with little direction, * 2 years experience as a in BI, analytics, or reporting ?
 * Strong SQL?
 * Hands-on Tableau experience or other dashboarding tools?
 * BS in computer science, engineering, mathematics, physics, or similar quantitative field?

## Description

* Ability to extract and manipulate data from multiple sources, developing assumptions where needed, to drive business insights and strategy
 * Build and own Tableau dashboards for originations, underwriting, collections, and the executive team
 * Respond quickly to inquiries from stakeholders; delivering conclusions with detailed assumptions that provide a clear understanding of the business question
 * Accumulates a deep understanding of processes that create data as well as how that data is used by applications and users
 * Turn vague stakeholder questions into scoped analyses with defensible numbers
 * Track down data discrepancies across source systems and build checks so they don't recur
 * Present findings directly to the people who act on them


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