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Insurance Data Analytics Jobs in Colorado (NOW HIRING)

Covetit

Reporting Data Analyst

Denver, CO · On-site

Act as a Data & Domain SME across P&C insurance subject areas including policy, claims, billing ... Strong domain expertise in Workers' Compensation and claims analytics. * 5+ years of experience ...

Propelus

Senior Data Analytics Engineer

Boulder, CO · On-site +1

$109K - $149K/yr

As an Analytics Engineer, you will play a crucial role in transforming raw data into actionable ... Employees can choose from HSA, FSA, and traditional insurance options for medical, dental, and ...

iLending

Director of Data and Analytics

Englewood, CO

$130K - $150K/yr

... D Insurance, Supplemental Life and AD&D Insurance, Employer Paid Short-Term and Long-Term ... The Director of Data Analytics will provide hands-on consultancy, collaboration, and support to all ...

SumUp

Data Analytics Engineer - Boulder, Colorado

Boulder, CO · On-site +1

$100K - $120K/yr

Data Analytics Engineer - Boulder, Colorado Full-time | Boulder, CO | No visa sponsorship available ... Health and life coverage: group medical coverage, plus life insurance * Financial security: 401(k) ...

kgs

Data Analyst

Denver, CO

... vision insurance, 401K with company matching, flexible spending accounts, paid holidays, three ... Perform longitudinal data analysis, including Program Information Report (PIR) analysis, and ...

kgs

Data Analyst

Denver, CO

... vision insurance, 401K with company matching, flexible spending accounts, paid holidays, three ... Perform longitudinal data analysis, including Program Information Report (PIR) analysis, and ...

Koniag, Inc.

Data Analyst

Denver, CO · On-site

... vision insurance, 401K with company matching, flexible spending accounts, paid holidays, three ... Perform longitudinal data analysis, including Program Information Report (PIR) analysis, and ...

Cymertek

Data Analyst

Aurora, CO · On-site

... Analytics, Operations Research, Finance, Engineering, etc. ALTERNATE EXPERIENCE General comment on ... No cost to you. * Life Insurance - 100% employee coverage. No cost to you. * Additional ...

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Insurance Data Analytics information

What are the key skills and qualifications needed to thrive in the Insurance Data Analytics position, and why are they important?

To thrive in Insurance Data Analytics, you need a solid understanding of data analysis, statistics, and insurance industry concepts, usually supported by a degree in mathematics, statistics, finance, or a related field. Proficiency with analytical tools like SQL, Python, R, and data visualization platforms (such as Tableau or Power BI), as well as certifications like CPCU or advanced analytics credentials, are highly valued. Strong problem-solving abilities, attention to detail, and effective communication skills help analysts translate complex data into actionable business insights. These skills are crucial for driving informed decision-making, risk assessment, and operational improvements within insurance organizations.

What are the typical responsibilities of someone working in Insurance Data Analytics?

Professionals in Insurance Data Analytics are responsible for collecting, cleaning, and analyzing large sets of insurance-related data to identify trends, assess risk, and inform business decisions. They commonly develop predictive models, generate reports, and provide actionable insights that help underwriting teams, actuarial staff, and business leaders optimize processes or pricing strategies. Day-to-day tasks may also include collaborating with IT and business units to define data requirements, presenting findings to non-technical stakeholders, and ensuring data integrity. This role often involves a mix of independent analysis and team-oriented projects, offering a dynamic and engaging work environment for problem solvers.

How is data analytics used in insurance?

In insurance, data analytics is used by professionals to assess risk, set premiums, detect fraud, and improve customer segmentation. Analysts utilize tools like statistical models and machine learning algorithms to interpret large datasets, enabling more accurate underwriting and claims management. Strong analytical skills and knowledge of data visualization are essential for effective decision-making in this field.

What does a data analyst do in insurance?

An insurance data analyst collects, processes, and analyzes insurance data to identify trends, assess risks, and support decision-making. They use tools like Excel, SQL, and data visualization software to create reports and models that improve underwriting, claims management, and pricing strategies.

How much does an insurance analyst make?

The average salary for an insurance analyst is around $65,000 to $85,000 per year, depending on experience, location, and industry. Entry-level roles typically start lower, while experienced analysts with specialized skills or certifications can earn higher salaries. Strong analytical skills and proficiency with data tools like Excel or SQL are often required.

Will AI replace a data analyst?

AI can automate routine data processing and analysis tasks, but the role of a data analyst, including those in insurance data analytics, involves interpreting complex data, providing insights, and making strategic decisions that require human judgment. Therefore, AI is more likely to augment rather than fully replace data analysts, who also need skills in data visualization, domain knowledge, and communication. Continuous learning and proficiency with analytics tools remain important for the role.

What is an Insurance Data Analytics job?

An Insurance Data Analytics job involves analyzing large volumes of insurance-related data to identify trends, assess risks, detect fraud, and improve decision-making. Professionals in this field use statistical models, machine learning, and data visualization tools to extract insights that help insurers optimize pricing, enhance customer experience, and reduce losses. They work with claims data, policyholder information, and external data sources to drive business strategy. Strong analytical skills, proficiency in data tools like SQL, Python, or R, and knowledge of insurance principles are essential for success in this role.

What are the most commonly searched types of Insurance Data Analytics jobs in Colorado? The most popular types of Insurance Data Analytics jobs in Colorado are:
What are popular job titles related to Insurance Data Analytics jobs in Colorado? For Insurance Data Analytics jobs in Colorado, the most frequently searched job titles are:
What job categories do people searching Insurance Data Analytics jobs in Colorado look for? The top searched job categories for Insurance Data Analytics jobs in Colorado are:
What cities in Colorado are hiring for Insurance Data Analytics jobs? Cities in Colorado with the most Insurance Data Analytics job openings:
Insurance Data Analytics jobs near you
Infographic showing various Insurance Data Analytics job openings in Colorado as of July 2026, with employment types broken down into 100% Full Time. Highlights an 90% In-person, 5% Hybrid, and 5% Remote job distribution.
Reporting Data Analyst

Reporting Data Analyst

Covetit

Denver, CO • On-site

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Reporting Data Analyst

The Senior Reporting Data Analyst brings 13+ years of experience in enterprise data warehousing and reporting, with deep expertise in the Property & Casualty (P&C) insurance domain, especially Workers’ Compensation. Acts as a Subject Matter Expert (SME) on enterprise data warehouse (EDW) content and insurance source systems, ensuring accurate data interpretation, strong data lineage, and reliable, business-aligned reporting outputs.

Role Responsibilities:

  • Act as a Data & Domain SME across P&C insurance subject areas including policy, claims, billing, and financials.
  • Demonstrate deep expertise in Commercial Lines: Workers’ Compensation, General Liability, Commercial Auto, Property, Umbrella/Excess.
  • Collaborate with business stakeholders to gather, analyze, and validate reporting and data extract requirements.
  • Translate complex insurance business processes into data models, KPIs, metrics, and dimensional structures.
  • Leverage strong knowledge of Guidewire (PolicyCenter, ClaimCenter, BillingCenter), Genius, and SAP for accurate data interpretation and lineage.
  • Explain and map policy lifecycle events (submission, quote, bind, issue, endorsements, renewal, cancellation, reinstatement) into warehouse models.
  • Analyze claims lifecycle data including reserves, payments, recoveries, and claim financials.
  • Perform data lineage, source-to-target mapping, and transformation analysis across ETL and EDW layers.
  • Develop and validate complex SQL queries, views, and reporting datasets.
  • Ensure data reconciliation and consistency between transactional systems and enterprise warehouse.
  • Review and certify reports for accuracy, completeness, and regulatory compliance.
  • Investigate and resolve data discrepancies across source systems, ETL pipelines, and reporting layers.
  • Document data definitions, mappings, lineage, and business rules.
  • Support UAT, defect resolution, and reporting enhancements.
  • Educate stakeholders on data interpretation, reporting usage, and governance standards.

Technical Qualifications:

  • 13+ years of experience as a hands-on Reporting/Data Analyst in enterprise environments.
  • 10+ years working with Enterprise Data Warehousing (EDW/DWH) solutions.
  • 7+ years of experience in Property & Casualty Insurance (Commercial Lines).
  • Strong domain expertise in Workers’ Compensation and claims analytics.
  • 5+ years of experience with BI/reporting tools: MicroStrategy, Power BI.
  • Hands-on experience with Guidewire (PolicyCenter, ClaimCenter, BillingCenter), Genius, SAP.
  • Advanced SQL skills with extensive experience querying large-scale data warehouses.
  • Strong understanding of:
    • Data lineage and traceability
    • Source-to-target mappings
    • Dimensional modeling (facts, dimensions, aggregates)
    • Enterprise reporting architecture
    • Proven ability to reconcile data between source systems and warehouse environments.

General Qualifications:

  • Strong initiative with ability to self-manage in complex, ambiguous environments.
  • Excellent communication and stakeholder management skills.
  • Proven ability to work with senior business leaders and cross-functional teams.
  • Ability to clearly explain complex data concepts to technical and non-technical audiences.
  • Recognized as a trusted SME in Insurance Data & Data Warehousing.

Education Requirements:

  • Bachelor’s degree in Computer Science, MIS, or related field (or equivalent experience).

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