Data Engineer

COUNTRY Financial•Bloomington, IL

10d

About The Position

Designs, develops, and delivers enterprise data solutions that enable trusted, scalable, and secure data products across the organization. Leads creation and maintenance of solution architecture and technical design artifacts, including conceptual, logical, and physical data models, integration designs, data flows, interface specifications, and implementation roadmaps. Serves as a lead technical contributor within a SAFe Agile delivery team, collaborating with Product Management, Architecture, Engineering, Security, and business stakeholders to translate business capabilities and requirements into resilient data solutions. Uses modern data platforms, cloud-native technologies, data engineering frameworks, and analytics ecosystems to design and implement data products, pipelines, and services for operational, analytical, and AI-driven business outcomes. Applies DevSecOps principles, automation, and engineering best practices across the solution lifecycle, including source control, CI/CD, infrastructure as code, automated testing, observability, security controls, and production support. Uses AI-assisted development tools and practices to accelerate delivery, improve design quality and documentation, automate routine engineering activities, and promote continuous improvement. Establishes and supports data management standards, governance requirements, security controls, and quality practices to ensure solutions are scalable, compliant, maintainable, and aligned with enterprise architecture objectives. The role develops scalable, repeatable, and secure data pipelines for dashboards, machine learning models, and exploratory data analysis; designs and builds data models in Entity Relationship Diagrams (ERDs); prepares and manipulates data; manages data migrations and conversions; troubleshoots data processing issues; iteratively delivers functionality using DevOps tools and concepts; and collaborates with other areas to develop standards and best practices for research, analysis, data engineering, and machine learning.

Requirements

Typically requires 10+ years of relevant experience, or a combination of related experience, education, and training.

Responsibilities

Design, develop, and deliver trusted, scalable, and secure enterprise data solutions and products.
Create and maintain solution architecture and technical design artifacts, including data models, integration designs, data flows, interface specifications, and implementation roadmaps.
Collaborate with Product Management, Architecture, Engineering, Security, and business stakeholders to translate requirements into resilient data solutions.
Design and implement data products, pipelines, and services supporting operational, analytical, and AI-driven outcomes.
Apply DevSecOps principles and engineering practices across the solution lifecycle, including source control, CI/CD, infrastructure as code, automated testing, observability, security controls, and production support.
Use AI-assisted development tools to accelerate delivery, improve design and documentation, automate routine engineering activities, and support continuous improvement.
Establish and support data management standards, governance requirements, security controls, and quality practices.
Develop scalable, repeatable, and secure data pipelines for dashboard development, machine learning models, and exploratory data analysis.
Design and build data models in Entity Relationship Diagrams (ERDs).
Prepare and manipulate data; manage data migrations and conversions; troubleshoot data processing issues.
Iteratively deliver functionality using DevOps tools and concepts.
Collaborate across areas to develop standards and best practices for research, analysis, data engineering, and machine learning.