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Insurance Data Analyst Jobs in Tennessee (NOW HIRING)

Intellivo

Senior Data Engineer

Memphis, TN · On-site

$103K - $140K/yr

... insurance data. Responsibilities : • Design and build data ingestion pipelines from multiple ... analytics reporting. • Maintain Feature Store pipelines that produce machine learning-ready ...

Intellivo

Senior Data Engineer

Memphis, TN · On-site

$95K - $129K/yr

... and insurance data into structured intelligence signals used for identification scoring, analytics, and operational reporting. The role requires deep experience with cloud data platforms, strong ...

Intellivo

Senior Data Engineer

Memphis, TN

$95K - $129K/yr

... and insurance data into structured intelligence signals used for identification scoring, analytics, and operational reporting. The role requires deep experience with cloud data platforms, strong ...

Covenant Health

FP & ANALYTICS ANLST III

Knoxville, TN · On-site

Ensures appropriate mapping of data elements used in decision support such as transaction code mapping, insurance plans, department, payroll, and statistics for purposes of cost accounting, analytics ...

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All JobsInsurance Data Analyst JobsInsurance Data Analyst Jobs in Tennessee

Insurance Data Analyst information

See Tennessee salary details

$30.9K

$75K

$123.4K

How much do insurance data analyst jobs pay per year?

As of Jul 20, 2026, the average yearly pay for insurance data analyst in Tennessee is $75,006.00, according to ZipRecruiter salary data. Most workers in this role earn between $56,700.00 and $88,000.00 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.
What are popular job titles related to Insurance Data Analyst jobs in Tennessee? For Insurance Data Analyst jobs in Tennessee, the most frequently searched job titles are:
What job categories do people searching Insurance Data Analyst jobs in Tennessee look for? The top searched job categories for Insurance Data Analyst jobs in Tennessee are:
Insurance Data Analyst jobs near you
Infographic showing various Insurance Data Analyst job openings in Tennessee as of July 2026, with employment types broken down into 1% Locum Tenens, 1% Internship, 84% Full Time, 6% Part Time, 2% Temporary, and 6% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $75,006 per year, or $36.1 per hour.

Senior Data Engineer

Intellivo

Memphis, TN • On-site

$103K - $140K/yr

Full-time

Re-posted 3 days ago

Job description

Job Summary:
Intellivo is a company focused on transforming raw data into structured intelligence signals. They are seeking a Senior Data Engineer to design, build, and optimize scalable data pipelines and platform infrastructure within a medallion architecture, while ensuring compliance and data governance across healthcare, legal, and insurance data.
Responsibilities:
• Design and build data ingestion pipelines from multiple structured and unstructured sources including healthcare claims, P&C insurance data, and legal filings into the Bronze layer of the medallion architecture.
• Optimize ingestion workflows for reliability, throughput, and compliance across regulated production environments.
• Implement error handling, retry logic, and dead-letter patterns to ensure pipeline resilience.
• Develop Silver layer transformation logic including normalization, deduplication, entity resolution, and schema enforcement within Microsoft Fabric and OneLake.
• Build Gold layer aggregations and enriched datasets that support ML scoring models and embedded analytics reporting.
• Maintain Feature Store pipelines that produce machine learning-ready feature sets for model training and inference.
• Enforce data contractual constraints from third-party data providers, including requirements for stateless processing and restrictions on data persistence or model training.
• Implement multi-tenant data isolation patterns including partitioning, access controls, and governed data handling across a large number of client contracts.
• Document data lineage, transformations, and data contracts to support governance, audit readiness, and operational clarity.
• Build and maintain data quality validation scripts to detect schema drift, completeness gaps, and business-rule violations across pipeline stages.
• Implement monitoring on pipeline health, data freshness, and operational exceptions to maintain high-confidence production data.
• Establish alerting and escalation processes for pipeline failures and data anomalies.
• Partner with ML Engineering and Data Science to deliver features that support model retraining, scoring pipelines, and identification engine capabilities.
• Collaborate with Software Engineering, Analytics, and business stakeholders to translate operational needs into reliable, production-ready data solutions.
• Contribute to architectural decisions and technical documentation that support the broader data platform strategy.
Qualifications:
Required:
• B.S. or B.A. in Computer Science, Information Systems, Mathematics, or a related field.
• 7+ years of professional data engineering experience, preferably within Azure-based or Microsoft Fabric environments.
• Hands-on experience designing enterprise data pipelines, ETL/ELT workflows, and medallion or lakehouse architecture patterns.
• Strong programming skills in Python, with advanced SQL experience and data quality validation logic.
• Experience with Microsoft Fabric, OneLake, Azure Data Factory, or equivalent cloud data orchestration tools.
• Working knowledge of CI/CD practices for data pipelines and infrastructure-as-code concepts.
• Demonstrated experience using AI-assisted development tools (e.g., GitHub Copilot, Cursor, or similar) to accelerate pipeline development, code generation, and debugging workflows.
Preferred:
• Familiarity with healthcare data formats (claims, eligibility, EDI 837/835) and HIPAA compliance requirements.
• Experience with multi-tenant data architectures and governed data handling in regulated environments.
• Exposure to ML feature engineering, Feature Store design, or data pipelines supporting model training workflows.
• Experience with dbt, PySpark, or similar transformation frameworks.
Company:
Recovery Intelligence for health plans. Intellivo detects more reimbursement opportunities with no member abrasion. Founded in 1999, the company is headquartered in Memphis, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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