Senior Analyst Data Analytics

Overview The Data Analyst supports Risk & Integrity Services (RIS) by transforming complex operational, legal, claims, compliance, and

risk management data into actionable insights. This role develops reporting solutions, dashboards, data models, and

analytical tools that support executive decision-making, operational improvement, regulatory reporting, and strategic

initiatives.

The Data Analyst collaborates with business leaders, subject matter experts, and technology teams to collect requirements,

analyze data, identify trends, and deliver meaningful insights that improve organizational performance and reduce risk.

What you'll do?

• Own and manage incoming ServiceNow requests
• Analyze large and complex datasets to identify trends, anomalies, opportunities, and risks.
• Develop recurring and ad hoc reports for operational and executive stakeholders.
• Create and maintain Power BI dashboards, scorecards, and visualizations.
• Perform data validation and quality assurance activities.
• Support monthly, quarterly, and annual reporting processes.
• Maintain data integrity across reporting platforms and data marts.
• Assist with data governance and documentation efforts.
• Create and maintain data dictionaries, KPI definitions, and reporting standards.
• Support executive dashboards and strategic initiatives.

Who are we looking for? Required Education

• Associate’s degree + 3 years of experience in software development, data engineering, data governance, or similar OR
• Bachelor’s degree years of experience in software development, data engineering, data governance, or similar

Required Minimum Experience

• 2+ years of experience in data analytics, business intelligence, reporting, or related roles.
• Experience using: SQL, Power BI, Microsoft Excel (advanced)
• Strong analytical and problem-solving skills.
• Excellent written and verbal communication skills.
• Ability to manage multiple priorities and deadlines.
• Strong documentation and communication skills

Measures of Success

• Leadership trusts analytics outputs without re‑validating the data
• Analysts spend time on insights and decision-support, not data cleanup
• Data issues are detected and addressed proactively
• Increase in quantity and quality of data products
• Shifted focus to loss avoidance over loss reporting
• Further centralization of data sources