Business Analytics Advisor

Cigna Healthcare

About The Position

We are seeking an experienced and highly analytical individual contributor to help modernize pricing capabilities through automation and emerging AI technologies. This role will focus on building and improving the technical foundations that support next-generation pricing analytics, with a particular emphasis on agentic AI tooling, development infrastructure, and scalable analytic workflows. A central priority for this role will be enabling experimentation with AI tools and methods to uncover new insights in rating data and support innovative analytic use cases. This position is best suited for someone who enjoys hands-on technical problem solving, building durable solutions, and modernizing legacy processes through thoughtful engineering and automation. This is not a people management role. It is a deeply technical advisor-level individual contributor position for someone who can work independently, translate high-level priorities into practical solutions, and help establish a more modern analytics environment for pricing and underwriting teams.

Requirements

Bachelor’s degree in Computer Science, Data Analytics, Information Systems, Engineering, Statistics, Actuarial Science, or a related quantitative or technical field; equivalent practical experience will also be considered.
6+ years of relevant experience in analytics engineering, data engineering, automation, advanced analytics, or technical enablement roles.
Strong hands-on experience with GitHub and modern code management practices, including branching, pull requests, repository structure, and workflow automation such as GitHub Actions.
6+ years of experience with Python for data analysis, automation, workflow development, or technical solutioning.
6+ years of experience with SQL and working with structured data in relational and analytical environments.
Experience designing, building, or supporting data pipelines, transformation workflows, or orchestration frameworks using tools such as Airflow, Databricks, DBT, or comparable platforms.
Demonstrated ability to work within legacy environments and identify practical, scalable paths toward automation and modernization.
Strong technical problem-solving skills and the ability to independently structure and execute complex work.
Excellent attention to detail, especially in code quality, documentation, process design, and maintainability.
Strong written communication skills, with the ability to clearly document technical logic, workflows, and solution design.

Nice To Haves

Experience in insurance, healthcare, actuarial, pricing, underwriting, or other data-intensive analytical environments.
Exposure to AI-assisted development and automation tools such as Claude, n8n, Devin, Cursor, or similar technologies.
Familiarity with concepts related to agentic AI, LLM-enabled workflows, prompt-driven automation, or tool orchestration.
Experience creating reusable analytics assets, datamarts, or scalable data models that support self-service analysis.
Experience replacing spreadsheet-based or database-heavy legacy workflows with more governed and scalable technical solutions.
Familiarity with modern application, data, or cloud-based analytics architecture.

Responsibilities

Build and enhance technical infrastructure that supports modern pricing, including AI-enabled and agentic workflows.
Develop, test, and refine experimental analytic solutions designed to generate new insights from rating data and improve pricing processes.
Support strategic modernization efforts related to the disruptive rating engine project and other next-generation pricing capabilities.
Establish and improve GitHub-based development practices, including source control, versioning, reusable code structures, and workflow automation.
Help migrate legacy analytic processes away from fragile or manual tools such as Access databases, macro-enabled workbooks, and other intermediary technical solutions.
Design and implement automation solutions that reduce manual effort, improve consistency, and increase the reliability of recurring analytic and operational workflows.
Contribute to the development of analytic datasets, datamarts, and data structures that improve usability, scalability, and self-service analytics.
Help formalize data, documentation, and process standards to improve transparency, reproducibility, and long-term maintainability.
Evaluate and apply modern AI development tools where appropriate to accelerate prototyping, workflow execution, and technical experimentation.
Produce clear technical documentation for code, data pipelines, workflows, and solution design to support governance and operational sustainability.
Operate with a high degree of autonomy while aligning work to defined priorities and modernization goals.