• As a backend/Full-stack Software engineer, you'll be working as part of a close-knit product engineering team to own, build, and deliver features that drive real user impact. • Build services that matter - Own, design and ship microservices that power new features and improve the solutions that help bring meaningful health & wellbeing improvements to our users. • Own what you build - Partner with platform teams to deploy, monitor, operate and troubleshoot your code in production—you'll have real ownership from commit to customer. • Shape the product - Work directly with product, design, and data teams throughout the entire lifecycle—your technical perspective will influence what we build and how we build it. • Level up the team - Actively improve how we work through peer reviews, design sessions, incident response, and mentoring—be part of building something bigger than any single feature.

• Own the delivery and long-term operation of high-impact backend systems • Set and evolve backend processes, engineering standards, and technical best practices • Define and actively use KPIs covering system reliability, development speed, data quality, and error rates • Make system-level architectural decisions and technical tradeoffs with a clear understanding of impact • Establish automated testing and validation processes that reduce regressions and operational risk

• Design, develop, test, and maintain applications using .NET technologies • Build and consume RESTful APIs and web services • Work with databases to design schemas, write queries, and optimize performance • Collaborate with frontend developers, QA teams, and product managers • Participate in code reviews and ensure best coding practices • Troubleshoot, debug, and optimize existing systems • Ensure application security, scalability, and performance • Provide regular progress updates and documentation

• Build and maintain data pipelines using Apache Spark on Databricks • Write clean and efficient Java (Java 8 or higher) following best coding practices • Work with large structured and semi-structured data sets • Tune Spark jobs to improve performance, stability, and cost • Partner with other teams to understand requirements and deliver solutions • Make sure data handling follows security and governance standards • Investigate and fix issues in production environments