Software Engineering Senior Advisor - Hybrid

The Cigna Group•Austin, CA

4d•Hybrid

About The Position

Bring your technical leadership and passion for modern engineering to a role where your work directly improves how critical data and reporting platforms perform at scale. As a Senior Software Engineering Advisor, you will guide the evolution of enterprise reporting systems, modernize data platforms, and integrate AI-enabled capabilities that increase reliability, speed, and developer productivity. You will lead through influence and expertise, partnering closely with engineering, product, and business stakeholders.

Requirements

Minimum of 10 years of experience in enterprise software or data engineering roles supporting large-scale, client-facing platforms.
Strong expertise in SQL, ETL frameworks, data modeling, and cloud-based data pipelines.
Hands-on experience with platforms such as AWS, Snowflake, Databricks, Teradata, or enterprise BI and reporting tools.
Proven ability to support production systems, optimize performance, and improve reliability.
Experience working in Agile environments and collaborating with cross-functional teams.
Healthcare industry experience is required; PBM or PBS experience is strongly preferred.
Strong communication and partnership skills, with the ability to influence without direct authority.

Nice To Haves

Experience building or supporting AI/ML solutions using Python.
Familiarity with retrieval-augmented generation (RAG) architectures and vector search technologies.
Experience supporting mainframe or batch processing environments.
Bachelor’s degree in Computer Science, Engineering, or a related field is preferred, or equivalent practical experience.

Responsibilities

Lead the design, development, and modernization of enterprise reporting platforms, ETL pipelines, and data transformation solutions to deliver accurate, timely, and scalable outcomes.
Drive platform modernization initiatives, including migration from legacy environments to cloud-based solutions such as Databricks and Snowflake.
Integrate AI-enabled capabilities, including Python-based services and retrieval-augmented generation (RAG) and vector search solutions, to improve reliability, automation, and issue resolution.
Provide architectural leadership for high-volume data processing and reporting systems, ensuring solutions are resilient, observable, and efficient.
Champion modern engineering practices, including Agile delivery, DevOps automation, and continuous improvement, to elevate quality and developer experience.
Partner with product, reporting, analytics, and business stakeholders to translate complex requirements into clear, actionable engineering solutions.
Guide production support, incident triage, and root-cause analysis to strengthen operational stability and data trust.
Ensure adherence to enterprise engineering standards, security policies, and data governance requirements.
Communicate clearly with both technical and non-technical audiences, providing transparency on progress, risks, and outcomes.