Data Analyst - Mid

Job Summary:

We are looking for a skilled and proactive Mid-Level Data Analyst to support enterprise-wide reporting, business insights, and data quality initiatives. The role will involve developing dashboards, performing in-depth analyses, and working closely with stakeholders across operations, finance, and risk teams to ensure decisions are driven by accurate, timely data.

The ideal candidate brings experience in data transformation, visualization, and communication of analytical findings, especially in financial services, insurance, or shared services environments.

Key Responsibilities:

• Analyze business trends and performance metrics to generate actionable insights and decision support.
• Design and maintain interactive dashboards and standardized reports using Power BI, Tableau, or similar tools.
• Extract, clean, and manipulate large datasets from various sources (e.g., SQL databases, cloud platforms, Excel).
• Collaborate with business units to understand reporting needs and translate them into analytical solutions.
• Monitor data integrity, reconcile inconsistencies, and contribute to data governance processes.
• Automate recurring reports and support ad hoc data requests with accuracy and timeliness.
• Document report logic, data sources, and dashboard usage to support transparency and reuse.
• Stay current on data tools and trends to continuously improve reporting processes and insights delivery.

Qualifications:

• Bachelors degree in Data Science, Statistics, Business Analytics, Economics, or a related field.
• 3-5 years of experience in a data analysis or reporting role, preferably in insurance, reinsurance, finance, or shared services.
• Strong proficiency in SQL and experience with data visualization tools such as Power BI, Tableau, or Looker.
• Strong Excel skills (pivot tables, formulas, data cleaning).
• Familiarity with Python, R, or other scripting tools for advanced data transformation is a plus.
• Understanding of data modeling, KPIs, and business intelligence best practices.
• Excellent analytical thinking and attention to detail.
• Strong verbal and written communication skills; able to explain data findings to non-technical users.