A HealthTech client of IG is seeking a Staff Software Engineer to join their R&D innovation team. You will play a key role in shaping and implementing the technology strategy across Ensemble software delivery teams. You will architect and develop scalable, resilient, and reusable software solutions that accelerate delivery and improve engineering efficiency. This role requires a deep understanding of modern software engineering patterns, a strong desire to mentor others, and a passion for elevating engineering practices.
You will work closely with product, architecture, and engineering leadership to translate business objectives into actionable technical solutions. Through hands-on design sessions, technical prototyping, and code reviews, you will influence engineering behaviors and help establish high standards for quality, security, performance, and maintainability. Your work will directly impact the organization’s ability to achieve long-term strategic goals.
Relocation to San Jose is required for the role. Relocation assistance is available for eligible candidates.
Essential Job Functions
- Design, develop, test, deploy, monitor, and continuously improve high-quality software solutions using modern engineering practices.
- Build scalable, maintainable, and reusable components, patterns, frameworks, and tooling that address cross-cutting needs across multiple delivery teams.
- Collaborate with product and design teams to translate product concepts into technical designs and incremental deliverables that provide frequent, high-quality customer value.
- Partner with architecture to help establish, document, and advocate for technical standards, design patterns, and best practices.
- Participate in and help lead technical design sessions, spike investigations, and architecture reviews, ensuring alignment with long-term platform strategy.
- Engage in code reviews to ensure code quality, promote best practices, and mentor engineers through constructive, actionable feedback.
- Troubleshoot complex, multi-system issues across distributed architectures, driving sustainable long-term fixes.
- Contribute to a culture of engineering excellence by promoting automation, observability, testing, security-first design, and continuous improvement.
- Help evaluate emerging technologies, frameworks, and vendor solutions, and provide guidance on their potential impact or value.
Qualifications
Required Skills & Experience
- Bachelor’s degree in Computer Science, Statistics, Mathematics, or a related technical field; advanced degrees preferred.
- 7+ years of hands-on software engineering experience building production-grade applications using JavaScript/TypeScript, .NET Core, or similar languages, with a demonstrated ability to learn new technologies quickly.
- Deep understanding of core engineering fundamentals including automated testing, CI/CD, code quality, observability, DevOps practices, and iterative delivery.
- Experience working with at least three or more of the following:
- Continuous Integration & Continuous Delivery (CI/CD) platforms
- RESTful API development and/or GraphQL
- Serverless architectures (AWS Lambda, Azure Functions, etc.)
- Containerization and orchestration (Docker, Kubernetes)
- Infrastructure as Code (IaC) technologies (Terraform, CloudFormation, Bicep)
- Public cloud platforms (AWS, Azure, GCP)
- Application observability and monitoring tooling
- Event-driven or streaming architectures (Kafka, EventBridge, Pub/Sub)
- Proven experience building and supporting applications using componentized, microservices, or distributed architectures.
- Strong written and verbal communication skills, with the ability to explain complex technical concepts to both technical and nontechnical audiences in a globally distributed organization.
- Demonstrated knowledge of software architecture principles, design patterns, and engineering best practices.
- Exposure to AI-enabled development tools, code generation models, or ML-driven insights (e.g., Copilot, embedding models, vector search).
- Exposure to AI-enabled development tools, code generation models, or ML-driven insights (e.g., Copilot, embedding models, vector search).