Software Gigs

Staff Software Engineer role designing and implementing cloud-native platform components and services, with primary focus on Kubernetes workloads, distributed systems, and infrastructure automation. Requires 5+ years hands-on Kubernetes experience, proficiency in Go or systems languages, and demonstrated expertise building operators, controllers, and platform services. Will lead well-scoped projects from design through production, contribute to reliability and scalability, mentor junior engineers, and participate in on-call operations.

As a Staff Software Engineer in Data Platform Engineering, you will design, build, and operate cloud-native platform components and services, taking features from design through production. You'll lead well-scoped technical projects, collaborate with senior engineers on architecture alignment, and ensure reliability and scalability through testing and instrumentation. You'll spend most of your time hands-on building operators, controllers, automation, and platform services that other teams depend on, while mentoring junior engineers. The role requires 8+ years of software development experience (or equivalent with advanced degrees), 5+ years of hands-on Kubernetes experience, and proficiency with at least one major hyperscaler (AWS, Azure, GCP).

Senior individual contributor responsible for end-to-end demand forecasting across Intuit's Global Business Solutions Group portfolio. Build and maintain predictive models incorporating time-series analysis, regression, causal inference, and ML techniques to translate business signals into accurate, decision-ready demand forecasts at multiple granularities. Conduct A/B/n experiments to validate modeling choices, quantify forecast risk through scenario analysis, and act as trusted partner between technical teams and business stakeholders (Workforce Management, Capacity Planning, Finance, Marketing, Product) to ensure forecasts drive downstream capacity and expense planning. Continuously advance forecasting methodology, evaluate where AI/ML meaningfully improves accuracy, and support real-time in-season re-forecasting and analytics.