Business Data Analyst (Mid-Level)

At Tara, data is central to how we understand our business and make decisions. We’re looking for a mid-level Business Data Analyst to partner with teams across the company and use data to guide strategy, execution, and prioritization.

In this role, you’ll work closely with product, business, marketing, and leadership to frame business questions, define meaningful metrics, and analyze data related to users, merchants, transactions, and credit products. Your analyses will be used directly in business discussions and decision-making, requiring you to interpret results, explain trade-offs, and make practical, data-backed recommendations.

This is a hands-on analytical role with growing ownership over high-impact analyses.

Responsibilities:

• Translate open-ended business questions into structured analytical problems
• Define, maintain, and evolve metrics that reflect business performance
• Analyze data to diagnose issues, compare segments, and identify opportunities
• Evaluate the impact of incentives, programs, and operational changes
• Build clear analyses and visualizations for managers and cross-functional teams
• Communicate insights, assumptions, and recommendations clearly and concisely

What We Look For:

Technical Skills:

• 3–5 years of experience in business analytics, data analytics, or a similar role
• Strong analytical thinking and business judgment
• Experience working with stakeholders and influencing decisions through data
• Proficiency in SQL and a BI tool such as Power BI
• Working knowledge of Python for data analysis
• Ability to communicate findings effectively to technical and non-technical audiences

 
Soft Skills:

• Strong analytical and problem-solving mindset.
• High attention to detail and data accuracy.
• Proactive and curious learner with a growth mindset.
• Effective communication and teamwork skills.
• Ability to manage multiple projects simultaneously.
• Team player with excellent communication skills.
• Responsible and committed to deadlines.

Nice to have:

• Experience in fintech, lending, credit, or marketplaces
• Familiarity with experimentation or applied machine learning techniques
• Exposure to modern AI tools used in analytics workflows