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Insurance Data Analyst Jobs (NOW HIRING)

Required Qualifications * 5+ years of experience working with insurance bordereaux (BDX) reporting, premium reporting, or insurance data analysis. * Strong understanding of MGA, Program, P&C, or ...

Senior Insurance Data Analyst

Irving, TX ยท On-site

$82K - $104K/yr

JOIN OUR TEAM AS A SENIOR INSURANCE DATA ANALYST!! We believe analytics isn't a back-office function. It's not a reporting layer bolted onto the business after the real decisions get made. It's the ...

8+ years as a hands-on IT Reporting Data Analyst 4+ years in reporting/data analysis within Property & Casualty insurance (reinsurance experience a plus) 3+ years hands-on experience with reporting ...

New

... analyst tool/IDQ Knowledge of XML and other databases like DB2, teradata and SQL ... Experience in insurance data models Qualifications Excellent written and verbal communication ...

Company Overview World Insurance Associates ("World") is a unique financial services organization ... Position Overview We are seeking a Data Migration Analyst to support the successful migration of ...

Hands-on experience with insurance loss data, loss development, exposure analysis, and rate adequacy. * Experience building models that have been deployed in production and used to make real ...

PitchBook's Data Analyst works with private markets transaction data to develop meaningful insights ... STD, LTD, AD&D, and life insurance Emotional Health * Paid sabbatical program after four years

PitchBook's Data Analyst works with private markets transaction data to develop meaningful insights ... STD, LTD, AD&D, and life insurance Emotional Health * Paid sabbatical program after four years

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Insurance Data Analyst information

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$34K

$82.6K

$136K

How much do insurance data analyst jobs pay per year?

As of Jul 8, 2026, the average yearly pay for insurance data analyst in the United States is $82,640.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,500.00 and $97,000.00 per year, depending on experience, location, and employer.

How much does an insurance analyst make?

An insurance data analyst typically earns between $60,000 and $85,000 annually, depending on experience, location, and certifications. Entry-level positions may start lower, while experienced analysts with advanced skills or specialized knowledge can earn higher salaries. They often work with data analysis tools like Excel, SQL, and statistical software in an office environment.

What is the difference between Insurance Data Analyst vs Actuary?

AspectInsurance Data AnalystActuary
Required CredentialsBachelor's degree in statistics, data science, or related field; often certifications like CAP or CPCUBachelor's degree in mathematics, statistics, or actuarial science; professional actuarial exams and credentials (e.g., ASA, FSA)
Work EnvironmentData analysis teams within insurance companies, focusing on data modeling and reportingActuarial departments, focusing on risk assessment, pricing, and reserving
Employer & Industry UsageInsurance companies, brokers, and consulting firmsInsurance companies, consulting firms, government agencies

While both roles involve working with insurance data, Insurance Data Analysts focus on data collection, analysis, and reporting, whereas Actuaries specialize in risk modeling and financial forecasting using advanced mathematics and actuarial exams. The roles often collaborate but serve different strategic functions within insurance organizations.

Do insurance companies need a data analyst?

Insurance companies rely on data analysts to interpret large datasets, assess risk, and support decision-making processes. Data analysts use tools like SQL and Excel, and often require industry knowledge and analytical skills to improve underwriting, claims management, and pricing strategies.

What are the key skills and qualifications needed to thrive as an Insurance Data Analyst, and why are they important?

To thrive as an Insurance Data Analyst, you need strong analytical skills, proficiency in statistics, and a background in mathematics, finance, or a related field, often supported by a relevant degree. Familiarity with data analysis tools such as SQL, Excel, Python, and data visualization platforms, as well as knowledge of insurance-specific databases and certifications like CPCU or AIDA, is highly valued. Attention to detail, problem-solving, and effective communication are crucial soft skills for interpreting complex data and presenting insights to stakeholders. These skills ensure accurate risk assessment, data-driven decision-making, and effective support of business objectives in the insurance industry.

How does an Insurance Data Analyst typically collaborate with underwriters and actuaries?

Insurance Data Analysts frequently work alongside underwriters and actuaries to provide data-driven insights that inform risk assessment and pricing decisions. Analysts gather, clean, and interpret large sets of policyholder and claims data, then present actionable findings through reports or dashboards. Regular meetings and joint projects ensure that underwriters and actuaries have the most accurate, up-to-date information to make decisions, and that data models align closely with business needs. Strong communication and teamwork skills are essential for success in this collaborative environment.

What does an Insurance Data Analyst do?

An Insurance Data Analyst is responsible for collecting, processing, and analyzing data related to insurance policies, claims, customer behavior, and market trends. They use statistical tools and software to identify patterns, assess risks, and provide actionable insights that help insurance companies make informed decisions. Their work supports pricing strategies, fraud detection, customer retention, and overall business performance. Insurance Data Analysts often collaborate with underwriters, actuaries, and business managers to optimize processes and improve profitability.

What does a data analyst do in insurance?

An insurance data analyst examines large datasets to identify trends, assess risk, and support decision-making for insurance policies and claims. They use tools like Excel, SQL, and data visualization software to interpret data and improve underwriting, pricing, and fraud detection processes.

Is 40 too late for data science?

For an Insurance Data Analyst, age is not a barrier to entering data science. Many professionals successfully transition into data roles later in their careers by developing relevant skills such as programming, statistics, and data visualization, often through online courses or certifications. Experience in insurance or related fields can also be valuable in this career path.
More about Insurance Data Analyst jobs
What cities are hiring for Insurance Data Analyst jobs? Cities with the most Insurance Data Analyst job openings:
What states have the most Insurance Data Analyst jobs? States with the most job openings for Insurance Data Analyst jobs include:
Infographic showing various Insurance Data Analyst job openings in the United States as of July 2026, with employment types broken down into 1% Locum Tenens, 1% Internship, 86% Full Time, 6% Part Time, 1% Temporary, and 5% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $82,640 per year, or $39.7 per hour.

Contractor

Re-posted 24 days ago


Job description


Title: Insurance Data Analyst
Duration: 2 Months
Location: Portland, OR
Job Description
The Insurance Data Analyst contractor will play a key role in supporting our transition to Riskonnect by gathering, validating, and analyzing insurance related data required for system configuration and ongoing reporting.
Requirements
Responsibilities
  • This role involvesconsolidating information from claims, policies, exposures, and historicalloss records; performing data quality checks; identifying inconsistenciesor gaps; and preparing structured datasets aligned with Riskonnect's datamapping and upload requirements.
  • The contractor will leverageadvanced Excel skills-including complex formulas, data cleansingtechniques, pivot tables, and data validation tools-to efficientlytransform and audit large datasets prior to migration.
  • They will collaborate closelywith internal stakeholders and the implementation team to ensure accuratedata migration, support user acceptance testing with analytical insights,and document data processes to enable smooth adoption of the new platform.
  • Clear communication,meticulous attention to detail, and the ability to work independently in afast-moving implementation environment are essential.