ZipRecruiter

Log In

1

Insurance Data Engineer Jobs in Oklahoma (NOW HIRING)

Addison Group

Data Engineer II

Tulsa, OK · On-site

$120/hr

Health insurance Disability insurance Life insurance Retirement plans (like a 401(k)) Paid time off (PTO) including vacation and sick days Summary: The Data Engineer is responsible for designing ...

Addison Group

Data Engineer II

Tulsa, OK · On-site

$120/hr

Health insurance Disability insurance Life insurance Retirement plans (like a 401(k)) Paid time off (PTO) including vacation and sick days Summary: The Data Engineer is responsible for designing ...

Addison Group

Data Engineer II

Tulsa, OK · On-site

$120/hr

Health insurance Disability insurance Life insurance Retirement plans (like a 401(k)) Paid time off (PTO) including vacation and sick days Summary: The Data Engineer is responsible for designing ...

Canoo

Canoo Data Platform - Data Engineer

Oklahoma City, OK · On-site

$98K - $118K/yr

Job Title Canoo Data Platform - Data Engineer About Canoo Canoo's mission is to bring EVs to ... Comprehensive Health Insurance * Equity Compensation * Flexible Paid Time Off * Casual workplace ...

ONE Gas

IT Supervisor, Data Engineering

Tulsa, OK · On-site

What You Will Do Supervise and direct employees performing data engineering and analytics platform ... Basic and Optional Employee Term Life Insurance * Education Assistance and Tuition Reimbursement

Stem Xpert

Data Architect

Tulsa, OK

$58.25 - $75/hr

The company is primarily focused on information technology, engineering, healthcare, financial ... Science, Insurance, legal, healthcare, among others. It also offers outsourcing, consulting ...

next page

Showing results 1-20

All JobsInsurance Data Engineer JobsInsurance Data Engineer Jobs in Oklahoma

Insurance Data Engineer information

How much do insurance engineers make?

Insurance data engineers typically earn a median salary ranging from $80,000 to $120,000 annually, depending on experience, location, and industry. Senior roles or those with specialized skills in data pipelines, cloud platforms, and programming languages like Python or SQL can command higher salaries. Compensation may also include benefits such as bonuses and professional development opportunities.

What engineers make $500,000?

Senior data engineers, including those working in specialized fields like insurance data engineering, can earn $500,000 or more annually, especially with extensive experience, advanced skills in cloud platforms, and leadership roles. High compensation is often associated with seniority, complex data systems, and working in competitive markets or large organizations.

What are Insurance Data Engineers?

Insurance Data Engineers are professionals who design, build, and maintain data systems that support the needs of insurance companies. They are responsible for collecting, organizing, and processing large amounts of data from various sources to enable accurate risk assessment, pricing, claims analysis, and regulatory compliance. Their work helps insurers make data-driven decisions, improve efficiency, and enhance customer experiences by leveraging modern data technologies.

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

To thrive as an Insurance Data Engineer, you need strong expertise in data modeling, ETL processes, and a solid understanding of insurance data structures, typically supported by a degree in computer science, data engineering, or a related field. Proficiency with SQL, Python, big data platforms (like Hadoop or Spark), and experience with cloud data solutions such as AWS or Azure are commonly required, along with certifications like AWS Certified Data Analytics or Google Cloud Data Engineer. Excellent problem-solving, communication, and collaboration skills help you bridge technical and business needs while ensuring data quality. These abilities are essential for building robust data pipelines and enabling accurate data-driven decision making within insurance organizations.

What is the difference between Insurance Data Engineer vs Data Analyst in the insurance industry?

AspectInsurance Data EngineerData Analyst
Required CredentialsBachelor's in Computer Science, Data Engineering certificationsBachelor's in Statistics, Data Analysis certifications
Work EnvironmentDevelops data pipelines, manages databases, works with big data toolsInterprets data, creates reports, visualizes insights
Employer & Industry UsageInsurance companies, tech firms in insuranceInsurance firms, consulting agencies, analytics companies

Insurance Data Engineers focus on building and maintaining data infrastructure, while Data Analysts interpret data to provide insights. Both roles are essential in the insurance industry but serve different functions in data management and analysis.

How does an Insurance Data Engineer typically collaborate with actuarial and underwriting teams?

Insurance Data Engineers work closely with actuarial and underwriting teams to ensure that the data infrastructure supports accurate risk assessment and pricing models. They often translate business requirements from these teams into technical specifications, build data pipelines to source and clean relevant data, and assist in implementing predictive analytics tools. Regular communication and collaboration are essential, as data engineers help bridge the gap between raw data and actionable insights for decision-making. This teamwork not only streamlines workflow but also enables continuous improvement of insurance products and customer experience.

Is AI replacing data engineers?

AI is transforming the role of data engineers by automating routine tasks such as data cleaning and integration, but it does not replace the need for skilled professionals to design, manage, and oversee data infrastructure. Data engineers are essential for building scalable data pipelines, ensuring data quality, and implementing AI solutions effectively. Their expertise remains critical in managing complex data environments and integrating AI tools into business processes.

What engineers make 300,000 a year?

Senior data engineers, including those working in specialized fields like insurance data engineering, can earn $300,000 or more annually, especially with extensive experience, advanced skills in SQL, Python, cloud platforms, and certifications. High-level roles often involve leadership, complex data architecture, and strategic decision-making, typically in large organizations or with specialized expertise.
What are popular job titles related to Insurance Data Engineer jobs in Oklahoma? For Insurance Data Engineer jobs in Oklahoma, the most frequently searched job titles are:
What job categories do people searching Insurance Data Engineer jobs in Oklahoma look for? The top searched job categories for Insurance Data Engineer jobs in Oklahoma are:
What cities in Oklahoma are hiring for Insurance Data Engineer jobs? Cities in Oklahoma with the most Insurance Data Engineer job openings:
Insurance Data Engineer jobs near you
Data Engineer II

Data Engineer II

Addison Group

Tulsa, OK • On-site

$120/hr

Other

Medical, Life, Retirement, PTO

Re-posted 2 days ago

Job description

Title: Data Engineer II/III

Location: Tulsa, Oklahoma

Salary: $120-135k

Position is not eligible for sponsorship

Benefits:

Health insurance

Disability insurance

Life insurance

Retirement plans (like a 401(k))

Paid time off (PTO) including vacation and sick days

Summary:

The Data Engineer is responsible for designing, developing, testing, implementing, supporting, and maintaining enterprise data pipelines, data warehouses, data marts, and business intelligence solutions. This role supports data-driven decision-making by delivering reliable, scalable, and high-quality data assets across the organization.

The ideal candidate will have experience building and supporting data integration solutions, developing data models, and collaborating with business and technical teams to deliver actionable insights.

Key Responsibilities

Data Engineering & Development (50%)

  • Design, develop, enhance, and maintain data pipelines following established software development and data governance practices.
  • Connect to and analyze data from a variety of source systems to assess data quality, structure, and business value.
  • Develop automated processes for extracting, cleansing, transforming, and loading data into enterprise data platforms.
  • Build and maintain master data solutions that consolidate information from multiple sources into trusted, centralized datasets.
  • Create automated data quality monitoring, validation, and alerting processes.
  • Develop and publish data models and datasets for reporting, analytics, and business intelligence initiatives.
  • Utilize ETL tools, SQL, data modeling platforms, business intelligence technologies, and programming languages to support development efforts.
  • Adhere to established development standards, architecture guidelines, and best practices.
  • Collaborate with technical teams to support application integrations, data architecture initiatives, and enterprise reporting strategies.
  • Participate in Agile development processes and contribute to technical planning, user story refinement, and solution design discussions.
  • Support troubleshooting and root-cause analysis for complex data and system issues.

Technical Support & Operations (35%)

  • Provide support for data platforms, reporting systems, and business intelligence solutions.
  • Investigate and resolve technical issues using strong analytical, problem-solving, and customer service skills.
  • Analyze recurring issues and implement long-term solutions to improve system stability and performance.
  • Maintain accurate documentation of incidents, resolutions, processes, and system configurations.
  • Develop support documentation, user guides, and technical knowledge resources.
  • Gather feedback from stakeholders and recommend enhancements to improve functionality and user experience.
  • Participate in on-call support rotations and assist with critical issue resolution as needed.

Training & Professional Development (10%)

  • Continuously expand technical knowledge through training, self-study, industry research, and professional development opportunities.
  • Incorporate feedback from peers and leadership to improve technical and professional skills.
  • Stay informed on emerging technologies, industry best practices, and evolving data engineering methodologies.
  • Support the development of junior team members through mentoring, knowledge sharing, code reviews, and technical guidance.

Communication & Administration (5%)

  • Communicate effectively with technical and non-technical stakeholders.
  • Provide timely updates regarding project status, risks, and deliverables.
  • Prepare clear and professional technical documentation and business correspondence.
  • Maintain accurate records of project work, support activities, and time tracking.
  • Collaborate effectively within cross-functional teams and contribute to a positive team environment.

Qualifications

Required Education

  • High school diploma or equivalent.
  • Technical coursework or professional training in programming, database technologies, data engineering, or related disciplines.

Preferred Education

  • Bachelor's degree in Computer Science, Information Systems, Data Analytics, Mathematics, Engineering, or a related field.

Required Experience

  • 3–5 years of experience designing, developing, and supporting enterprise data solutions.
  • Experience with systems analysis, solution design, data integration, and implementation within a business environment.

Preferred Experience

  • 5+ years of professional experience in data engineering, data warehousing, business intelligence, or related fields.
  • Experience supporting enterprise-scale data platforms and troubleshooting complex technical environments.

Required Skills

  • Proficiency in SQL and one or more data integration, reporting, or programming technologies.
  • Experience with ETL development, data transformation, and data modeling concepts.
  • Strong analytical and problem-solving skills.
  • Understanding of software development and data management best practices.
  • Excellent communication, organization, and time management skills.
  • Ability to work independently while collaborating effectively across teams.

Preferred Skills

  • Experience with cloud-based data platforms, data warehouses, business intelligence tools, and modern analytics technologies.
  • Familiarity with Agile development methodologies.
  • Knowledge of master data management, data governance, and data quality frameworks.
  • Experience mentoring junior team members and contributing to technical leadership initiatives.
Addison Group is an Equal Opportunity Employer. Addison Group provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, gender, sexual orientation, national origin, age, disability, genetic information, marital status, amnesty, or status as a covered veteran in accordance with applicable federal, state and local laws. Addison Group complies with applicable state and local laws governing non-discrimination in employment in every location in which the company has facilities. Reasonable accommodation is available for qualified individuals with disabilities, upon request.

Apply