Data Analyst
On-siteCharlotte, North Carolina, United States
Job Summary
Turn business questions from underwriting, claims, actuarial, and finance teams into well-structured analyses and repeatable reports. Build and maintain dashboards and scorecards for KPIs such as loss ratio, combined ratio, premium growth, and claims cycle time using Power BI or Tableau. Perform exploratory analysis to surface trends, anomalies, and opportunities, then document assumptions and methodology to ensure transparency. Analyze policy, premium, and claims data to support pricing and reserving discussions while partnering with actuarial teams to validate results. Write optimized SQL queries to extract and aggregate data from cloud data warehouses, profile datasets, and resolve data quality issues. Translate complex findings into concise narratives for non-technical stakeholders and enable self-service by curating trusted data sources.
Required Qualifications
- 4–7+ years of experience in data analysis, business intelligence, or reporting
- Strong, demonstrable SQL skills (complex joins, window functions, aggregations, query tuning)
- Proven experience building dashboards and reports in Power BI and/or Tableau
- Solid understanding of data modeling concepts (dimensional / star schema) from a consumer's perspective
- Ability to translate ambiguous business questions into structured analysis and clear deliverables
- Strong written and verbal communication with business stakeholders
- Strong SQL complex joins, window functions, aggregation, query tuning
- Power BI and/or Tableau dashboard and report development
- Data modeling literacy dimensional / star schema (consumer perspective)
- Ability to translate business questions into structured analysis
- Strong stakeholder communication and storytelling
- 4–7+ years in data analysis / BI / reporting
Desired Qualifications
- Working knowledge of Python (pandas) for data wrangling is a plus
- Insurance domain knowledge P&C and/or Life & Annuities; familiarity with premium, claims, and loss-ratio concepts
- Experience with cloud data platforms (Databricks, Snowflake, Azure/AWS/GCP)
- Python (pandas) or R for analysis and automation
- Statistical analysis fundamentals and A/B or cohort analysis experience
- Exposure to data governance, metadata, and trusted-source / semantic-layer practices
- Awareness of PII/PHI handling and regulated-data sensitivity
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