Data Analyst

Job Details:

Duration: 6+ Months of Contract

Overview

Analyzes datasets to identify trends, patterns, and insights that inform business decisions. Coordinates proactively with the business to understand requirements, ask the right questions, and raise flags early.

• Work closely with business stakeholders including:
• Underwriting
• Claims
• Actuarial
• Finance
• Operations
• Understand reporting and analytics needs.
• Translate business questions and KPIs into:
• Data analysis
• Reports
• Dashboards
• Conduct requirement-gathering sessions.
• Prepare:
• Business Requirement Documents (BRDs)
• Reporting specifications
• Present insights, trends, and recommendations clearly to business and leadership stakeholders.

• Strong hands-on experience in Insurance, including:
• Property & Casualty (P&C)
• Life
• Annuity
• Reinsurance
• Solid understanding of:
• Policy lifecycle (Quote, Bind, Issue, Endorse, Renew)
• Claims lifecycle and claims financials
• Premiums, losses, reserves, exposure
• Billing and invoicing
• Experience with insurance platforms preferred:
• Guidewire
• Duck Creek
• Sapiens
• Similar platforms

• Strong experience as a Data Analyst in enterprise environments.
• Proficiency in SQL for querying and data analysis.
• Hands-on experience with BI tools:
• Power BI (preferred)
• Tableau / Qlik (or similar)
• Strong Excel skills:
• Pivot tables
• Formulas
• Data analysis
• Experience with relational databases:
• SQL Server
• Oracle
• PostgreSQL
• Familiarity with:
• Data warehousing
• Dimensional data models

• Analyze data related to:
• Policy lifecycle
• Claims
• Premiums
• Losses
• Exposure
• Reserves
• Billing
• Develop and maintain:
• Dashboards
• Reports
• Scorecards
• Utilize BI tools such as:
• Power BI
• Tableau (or similar)
• Perform:
• Trend analysis
• Variance analysis
• Root-cause analysis
• Support:
• Regulatory reporting
• Operational reporting
• Management reporting
• Validate data accuracy and ensure consistency across reports.

• Write and optimize SQL queries to extract and analyze data.
• Collaborate with data engineering teams on:
• Data availability
• Data quality
• Data transformations
• Assist with:
• Data reconciliation
• Data quality checks
• Issue resolution
• Document:
• Data definitions
• Business rules
• Assumptions
• Metrics

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Contact:

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