Software Engineering Senior Advisor- Hybrid

Cigna Healthcare•Raleigh, NC

1d•Hybrid

About The Position

Cigna-Evernorth Services Inc. seeks a Software Engineering Senior Advisor for the Raleigh, NC location to design and build scalable data pipelines and ETL/ELT processes that can handle large amounts of both structured and unstructured data while ensuring security and reliability. The Cigna Group is dedicated to improving the health and vitality of those we serve through its divisions Cigna Healthcare and Evernorth Health Services, aiming to enhance the lives of clients, customers, and patients. The company has a tobacco-free policy and reserves the right not to hire tobacco/nicotine users in states where legally permissible.

Requirements

Bachelor’s degree in Computer Science, Electrical Engineering or related field and 5 years of experience in any software development role.
Master’s degree and 3 years of experience.
Hands-on experience in designing, building, and optimizing end-to-end applications using Python, Java, PL/SQL, APIs, and frameworks.
Software Development Life Cycle.
Clean code practices.
Debugging.
Unit testing.
Delivering scalable software.
Modern data ecosystems including Data Lakes, Lakehouse, ELT/ETL pipelines, streaming, and batch processing.
Building scalable data ingestion, transformation, orchestration, and optimization using cloud-native services including AWS and Databricks.
Tableau including building complex reports and hierarchical computations reports.
Performance optimization.
Report optimization.
Writing a data pipeline using PySpark.
Informatica PowerCenter components including source/target mapping, transformation, workflows, and performance tuning.
End-to-end ETL development including debugging, optimization, and data integration best practices.
Building scalable data pipelines using Spark, Delta Lake, and Notebooks.
Optimizing Spark jobs, managing clusters, and developing end-to-end ELT solutions on the Databricks Lakehouse platform.
End-to-end QA, including test strategy, test cases, automation, functional/regression testing, and defect management.
Modern automation frameworks.
CI/CD Integration.
Oracle(SQL) including complex query writing, performance tuning, indexing strategies, and optimizing large-scale relational databases.
AWS Engineering including API Gateway, Lambda, EC2, S3, IAM, and CloudWatch.
Cloud architecture fundamentals.
Data-focused services, including Glue and Athena.
Building scalable, secure cloud-native solutions.
Internet connection must be obtained through a cable broadband or fiber optic internet service provider with speeds of at least 10Mbps download/5Mbps upload if working from home.

Responsibilities

Create and maintain data warehouse and data lake solutions using on-prem and cloud platforms like AWS.
Design database structures and data models that perform well for analytical queries and reporting needs.
Write clear technical documentation and specifications that explain how our data systems work.
Research and recommend new data technologies and tools that can make our data infrastructure better and more efficient.
Build, test, and deploy data pipelines using industry-standard tools such as Data Bricks, Airflow, Kafka, and similar technologies.
Write and optimize SQL queries, stored procedures, and database components to extract and transform data efficiently.
Create solutions that process data both in real-time and in batches.
Set up automated checks to validate data quality and catch errors early in the pipeline.
Connect and pull data from various sources including APIs, different databases, files, and streaming data platforms.
Make data pipelines run faster and more cost-effectively through optimization.
Build frameworks and monitoring systems to ensure our data is accurate and trustworthy.
Create and enforce data governance policies that set standards for how we handle and manage data.
Implement security measures like encryption and access controls to protect sensitive data and ensure compliance with regulations such as GDPR, CCPA, or HIPAA.
Perform data audits to find and fix any data integrity problems.
Maintain documentation including data dictionaries, lineage tracking (showing where data comes from and where it goes), and metadata systems.
Guide and mentor junior data engineers and analysts, helping them grow their skills.
Review code written by team members to ensure quality and adherence to best practices.
Lead discussions about technical design decisions and review proposed architectures.
Work closely with software engineers and business analysts to understand what they need and deliver effective solutions.
Monitor data systems to ensure they're running smoothly and fix issues when they arise.
Find and eliminate performance bottlenecks that slow down data processing or storage.
Set up monitoring and alert systems so we know immediately when something goes wrong with our data pipelines.
Investigate the root causes of data incidents and put safeguards in place to prevent them from happening again.
Manage cloud resources efficiently to keep costs under control while maintaining performance.
Work with business stakeholders to understand their data needs and translate those needs into technical solutions.
Provide technical advice and consultation to product managers and business leaders.
Explain complex technical concepts in simple terms that non-technical tea members can understand.
Create clear documentation for technical processes, procedures, and system configurations so others can understand and maintain them.