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Insurance Data Analytics Jobs in Spring, TX (NOW HIRING)

Hp

Senior AI & Data Analytics

Spring, TX

$116K - $182K/yr

Senior AI & Data Analytics Description - Job Summary This role is responsible for overseeing the ... Dental insurance * Vision insurance * Long term/short term disability insurance * Employee ...

K2 Group Inc.

Data Analyst

Houston, TX · On-site

Requirements * Bachelor's degree in data analytics, statistics, information systems, security ... Medical/ Dental/ Vision Insurance; FSA Medical & FSA Dependent Care; Pre-tax 401(k) & ROTH 401(k) ...

Insolation Technology Ltd

Data Analyst

Houston, TX · On-site +1

$21 - $26/hr

The Data Analyst will be responsible for collecting, processing, and analyzing data to support ... Health Insurance * Life Insurance * Paid time off Schedule: * Monday to Friday Work location:

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All JobsInsurance Data Analytics JobsInsurance Data Analytics Jobs in Spring, TX

Insurance Data Analytics information

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$48

$84

How much do insurance data analytics jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for insurance data analytics in Spring, TX is $48.72, according to ZipRecruiter salary data. Most workers in this role earn between $39.13 and $55.19 per hour, depending on experience, location, and employer.

What is insurance data analytics?

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 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.

What are the key skills and qualifications needed to thrive in insurance data analytics?

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.

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 examines large datasets to identify trends, assess risk, and support decision-making processes within insurance companies. They use tools like Excel, SQL, and data visualization software to interpret claims, policy data, and customer information, helping improve underwriting, pricing, and fraud detection.

What are popular job titles related to Insurance Data Analytics jobs in Spring, TX?

For Insurance Data Analytics jobs in Spring, TX, the most frequently searched job titles are:

What job categories do people searching Insurance Data Analytics jobs in Spring, TX look for?

The top searched job categories for Insurance Data Analytics jobs in Spring, TX are:

What cities near Spring, TX are hiring for Insurance Data Analytics jobs?

Cities near Spring, TX with the most Insurance Data Analytics job openings:

Insurance Data Analytics jobs near you
Infographic showing various Insurance Data Analytics job openings in Spring, TX as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 19% Part Time, and 5% Contract. Highlights an 85% Physical, 1% Hybrid, and 14% Remote job distribution, with an average salary of $101,335 per year, or $48.7 per hour.

Data Analytics Engineer

DEXTER TECHNOLOGIES INC

Houston, TX • On-site

$95 - $130/hr

Other

Medical, Dental, Vision

Posted 14 days ago

Job description

Benefits:

  • Dental insurance

  • Health insurance

  • Vision insurance


Hi,


We are actively seeking qualified candidates for the following position for our client, who is an industry leader:


Data Analytics Engineer
Location: Houston TX (4 days in office)
Type: Full Time

This role is a hybrid, bridging the gap between data engineering and business intelligence. You'll spend part of your time writing and optimizing SQL — building staging tables, views, and fact/dimension models — and part of your time building semantic models and reports in Power BI. Keeping an eye on our scheduled data pipelines, you’ll handle first-line troubleshooting when something fails. You'll work closely with the Sr. Manager, Data & Analytics, and with business stakeholders across Finance, Operations, and other functions who depend on accurate, well-modeled data.


This is a great fit for someone who wants breadth — real ownership across the data stack — on a team small enough that your work visibly matters.


Key Responsibilities

  • Data modeling & SQL: Design, build, and maintain SQL views, staging tables, and fact/dimension models in our Azure SQL data warehouse, including deduplication and business-rule logic (e.g., status-based routing, multi-source reconciliation).

  • Semantic models & reporting: Build and maintain Power BI semantic models — relationships, DAX measures, security roles — and the reports and dashboards built on top of them for business stakeholders and company-wide reporting.

  • Pipeline support: Share responsibility with the Sr. Manager, Data & Analytics for monitoring scheduled Azure Data Factory pipelines and Power BI dataset refreshes. Respond to failures and perform basic troubleshooting.

  • Pipeline modifications: Make minor modifications to existing pipelines to support new or changing business requirements, as your familiarity with the tooling grows.

  • Business partnership: Partner with business SMEs to translate reporting requests and business logic (commission structures, revenue recognition, inventory rules, etc.) into accurate, well-documented data models.

  • Documentation: Write and maintain documentation for data models, metric definitions, and report logic so that data lineage and ownership are clear beyond any one person.

  • Standards & quality: Follow and help evolve team standards for naming conventions, DAX style, and semantic model design as the team's practices mature.



  • Continuous Improvement: Proactively identify opportunities for process improvements, optimize current data workflows, and incorporate new technologies or tools to enhance data analytics capabilities.


Knowledge and Skills
Required

  • 3+ years in a data-focused role (analytics engineer, BI developer, data analyst, or similar) with hands-on SQL work — comfortable with CTEs, window functions, and reading/writing complex, multi-source views.

  • Solid Power BI experience beyond report formatting — you've built semantic models from scratch and written DAX involving CALCULATE, filter context, and context transition, not just basic aggregations.

  • Dimensional modeling fundamentals (star schemas, slowly changing dimensions).

  • A track record of working directly with business stakeholders to translate ambiguous requirements or business rules into a working data model.

  • Strong attention to detail with data integrity — you double-check your joins and know how a bad join or an inclusive date boundary can quietly break a report.


Preferred

  • Exposure to an ERP or other core business system as a data source (order, invoicing, or GL data) - you understand that business rules, not just dates, often drive how records should be deduplicated or classified.

  • Understanding of ETL/ELT concepts and working knowledge of orchestration tools like Azure Data Factory or similar tools for automating data pipelines.

  • Familiarity with Microsoft Fabric Administration and Environment (Lakehouses, Dataflows Gen2) — not required, but a plus given our platform direction.

  • Basic Git / source control experience.

  • Python for data tasks.



  • Knowledge of data quality frameworks and data governance practices.


Qualifications

  • Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field. Master’s degree is a plus.

  • 3+ years in a data-focused role (analytics engineer, BI developer, data analyst, or similar) with hands-on SQL work — comfortable with CTEs, window functions, and reading/writing complex, multi-source views.


Flexible work from home options available.

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