Software Engineer-Remote

Optum is seeking a Software Engineer to support an Identity Management rebuild initiative and provide ongoing support for an existing application. In this remote role from San Francisco, CA, you will design, build, test, deploy, and operate scalable backend services and APIs in cloud environments, with an emphasis on observability and secure software delivery.

Key Responsibilities

• Design, develop, test, deploy, and support backend applications, RESTful APIs, and microservices for the Identity Management rebuild and the existing application.
• Build production-quality services using Python and modern API frameworks.
• Develop data-intensive components and distributed processing workflows using PySpark, Azure Databricks, and Azure Data Factory.
• Define API contracts and standards for request and response models, error handling, versioning, and service documentation using OpenAPI or Swagger.
• Implement resilient microservice patterns, including service-to-service communication, retries, timeouts, idempotency, asynchronous processing, and graceful failure handling.
• Create automated unit, integration, API, regression, and performance tests, and integrate quality checks into CI/CD pipelines.
• Implement logging, metrics, tracing, alerting, and dashboards to strengthen observability, incident response, and operational reliability.
• Apply secure coding practices, including authentication and authorization controls, secrets management, encryption, and privacy-by-design principles.
• Troubleshoot production issues, perform root cause analysis, improve service performance, and contribute to scalability and cost optimization.
• Apply Git-based engineering practices, including code reviews, branching strategies, automated builds, release controls, and infrastructure-aware deployment workflows.
• Use enterprise-approved AI tools and custom agents to accelerate coding, testing, documentation, debugging, monitoring, and operational support.
• Collaborate with product, data engineering, platform engineering, architecture, security, and application teams to deliver priorities and communicate risks, dependencies, and progress.

Required Qualifications

• 5+ years of hands-on software engineering experience building and operating enterprise applications, backend services, APIs, or microservices.
• 5+ years of hands-on experience with Python, including object-oriented programming, modular design, exception handling, packaging, automation, and maintainable coding practices.
• 3+ years of hands-on experience developing RESTful APIs and working with API specifications, validation, authentication, versioning, and documentation.
• 3+ years of hands-on experience with PySpark for distributed processing, transformation, data validation, and performance tuning.
• 2+ years of working experience with Azure Databricks or similar distributed data platforms for data-intensive use cases.
• 2+ years of working experience with relational and NoSQL databases, data modeling, SQL, transaction patterns, and application-level data access.
• Experience designing and building microservices, including service decomposition, interface design, scalability, resilience, and operational support.
• Experience with automated testing frameworks such as pytest and unit, integration, API, and regression testing practices.
• Git experience, including pull requests, code reviews, branching strategies, CI/CD fundamentals, release management, and production support.
• Experience with Docker and a working understanding of Kubernetes or managed container platforms.
• Experience implementing logging, monitoring, alerting, metrics, and distributed tracing for production services.
• Understanding of software design principles, common design patterns, clean code, code quality, and maintainable architecture.
• Basic working understanding of cloud security, identity and access management, data privacy, governance, and enterprise compliance concepts.
• Proven problem-solving and root cause analysis skills across application, API, data, and cloud layers.
• Proven communication and collaboration skills to explain technical decisions, risks, dependencies, and delivery progress clearly.

Technologies

• Python, PySpark, Azure, Azure Databricks, Azure Data Factory
• RESTful APIs, microservices
• OpenAPI, Swagger
• Git, CI/CD
• Docker, Kubernetes, Kubernetes or managed container platforms
• pytest
• SQL, NoSQL
• Object-oriented programming

Preferred Qualifications

• Experience with event-driven architecture and messaging technologies such as Kafka, Azure Event Hubs, Azure Service Bus, or comparable platforms.
• Experience supporting performance testing, capacity planning, secure software development lifecycle practices, and cloud cost optimization.
• Experience building identity management, identity resolution, consumer profile, member/customer identity, or entity-resolution applications.
• Hands-on experience with Azure Data Factory and data pipeline orchestration.
• Hands-on experience building or supporting custom AI agents for coding, testing, documentation, validation, monitoring, incident triage, or workflow automation.
• Healthcare, insurance, consumer profile, or enterprise identity data experience.
• Knowledge of caching, API gateways, load balancing, rate limiting, asynchronous programming, and high-availability design.

AI Skills and Usage Expectations

• Demonstrate baseline proficiency with enterprise-approved AI tools such as GitHub Copilot, Microsoft 365 Copilot, Databricks Genie, and other approved GenAI platforms, where applicable.
• Apply AI tools responsibly to improve coding, code review, test creation, technical documentation, debugging, analysis, monitoring, and delivery quality.
• Use or support custom agents for software development, automated testing, API validation, failure detection, incident triage, runbook execution, and operational reporting.
• Follow enterprise guidance for responsible AI use, data protection, human review, security, and software quality.

Success Profile

• Balances solid software engineering discipline with practical data engineering knowledge.
• Builds secure, observable, maintainable services that can be supported reliably in production.
• Works effectively across application, data, platform, security, and business teams.
• Continuously improves engineering productivity through automation, reusable components, and responsible use of AI-assisted development tools.

Compensation and Location

• Location: San Francisco, CA (remote)
• Salary: USD 72,800 - 130,000 per yearly
• Experience requirement: 5+ years

Benefits

• Comprehensive benefits package
• Incentive and recognition programs
• Equity stock purchase
• 401k contribution (all benefits are subject to eligibility requirements)