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Insurance Data Analyst Jobs in Oregon (NOW HIRING)

Overview The (Senior) Corporate Data Analyst is responsible for working with stakeholders all areas ... Fully Paid by Origami Risk - Vision insurance, Short & Long-Term Disability Insurance, and Basic ...

Overview The Data & Insights Analyst is responsible for leading the design, architecture, and ... insurance (or retail auditing equivalent). * Ability to translate business problems into analytical ...

Senior Data Analyst - Financial Analytics

OR ยท On-site +1

$132K - $173K/yr

We are seeking a highly skilled and motivated Senior Data Analyst to play a pivotal role in ... We offer a full benefits package, including medical, dental, vision, life insurance, disability ...

OR

$85K - $100K/yr

The Sr. Analyst, Data Visualization role supports the delivery of one of our most impactful ... insurance. Employees receive flexible PTO, as well as paid sick leave prorated based on hire date.

Job Summary & Responsibilities We are looking for a highly skilled Data Analyst with deep ... From health, dental, and vision coverage to life and disability insurance, PTO, and a 401(k) with ...

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Showing results 1-20

Insurance Data Analyst information

See Oregon salary details

$35.9K

$87.4K

$143.8K

How much do insurance data analyst jobs pay per year?

As of Aug 3, 2026, the average yearly pay for insurance data analyst in Oregon is $87,374.00, according to ZipRecruiter salary data. Most workers in this role earn between $66,100.00 and $102,600.00 per year, depending on experience, location, and employer.

How much does an insurance data analyst make?

The average salary for an insurance data analyst typically ranges from $60,000 to $85,000 per year, depending on experience, location, and certifications. Professionals with advanced skills in data analysis tools like SQL, Excel, or Python may earn higher salaries, especially in larger companies or competitive markets.

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.

What are the key skills and qualifications needed to thrive as an insurance data analyst?

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.
Infographic showing various Insurance Data Analyst job openings in Oregon as of July 2026, with employment types broken down into 67% Full Time, 7% Part Time, 25% Contract, and 1% Nights. Highlights an 61% Physical, 5% Hybrid, and 34% Remote job distribution, with an average salary of $87,374 per year, or $42 per hour.

Contractor

Re-posted 21 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.