Data Engineer

FMNE Insurance Company•Lincoln, NE

7d•Hybrid

About The Position

FMNE Insurance is seeking a Data Engineer to join our growing IT team. We’re looking for someone that’s collaborative and solutions-focused, who enjoys building reliable, scalable data solutions and solving complex data challenges. The ideal candidate will have hands-on experience with modern cloud data technologies and a strong commitment to data quality and continuous improvement. This position is not eligible for visa sponsorship. Applicants must be authorized to work in the United States on a full-time basis. Please submit a resume and cover letter for consideration. We offer a competitive salary and a comprehensive benefits package, including health coverage, a generous 401(k), pension plan, wellness programs and a hybrid work model for eligible employees. Role Overview of a Data Engineer: The Data Engineer plays a critical role in designing, building, and maintaining scalable data solutions to support enterprise analytics and operational reporting. This position focuses on building and maintaining data pipelines and architectures using modern cloud technologies such as Azure Data Factory, Databricks, and SQL Server, enabling the delivery of high-quality, well-governed data across the organization. The engineer collaborates closely with data analysts, software developers, business stakeholders, and architects to ensure that data is accessible, accurate, timely, and aligned with business goals.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Science, or a related field; or equivalent experience in data engineering roles.
Minimum 2–4 years of experience in data pipeline development, ETL/ELT, and database systems.
Proficiency in SQL and performance tuning within SQL Server or similar RDBMS platforms.
Hands-on experience with Azure Data Factory, Databricks, and related Azure data services.
Familiarity with Git, CI/CD pipelines, and Azure DevOps tools.
Experience with data modeling techniques, especially in dimensional modeling and schema design for analytics.
Strong understanding of data privacy, security, and governance concepts.
Familiarity with scripting languages such as Python or PowerShell is a plus.
Strong problem-solving skills with the ability to debug complex data workflows and recommend scalable solutions.
Effective verbal and written communication skills; ability to communicate technical concepts to business stakeholders.
Ability to manage multiple priorities, self-organize, and thrive in a collaborative Agile environment.

Nice To Haves

Experience with Data Lake architecture, delta tables, and real-time or near-real-time streaming.
Exposure to Power BI or similar data visualization tools.
Familiarity with metadata management, data catalogs, and data lineage tools.

Responsibilities

Design, develop, test, and maintain data pipelines and transformation workflows using Azure Data Factory and Databricks.
Build and optimize data models, tables, and views to support reporting, dashboards, and self-service analytics.
Ensure data reliability, availability, and performance through proactive monitoring, alerting, and tuning of ETL/ELT processes and databases.
Work with business and technical teams to gather data requirements, define data contracts, and ensure alignment across stakeholders.
Collaborate with Data Architecture and other teams to support data platform modernization and cloud-first strategies.
Support CI/CD practices through integration with Azure DevOps and automated data pipeline deployments.
Apply data quality, profiling, and cleansing techniques to ensure accuracy, completeness, and consistency of datasets.
Enforce data governance and security policies, including data classification, lineage, and role-based access controls.
Develop and maintain comprehensive technical documentation, including data dictionaries, pipeline diagrams, and support procedures.
Conduct root cause analysis on data issues and recommend long-term solutions to prevent recurrence.
Stay current on industry trends, tools, and best practices in the data engineering space.