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The Software Engineer II will develop Django-based websites using Python and work with large data sets while collaborating with teams to deliver high-quality work in a remote environment.

Lead definition and delivery of Turquoise Health's data products: define requirements, build roadmaps, prioritize features from customer feedback, collaborate with engineering and GTM teams, enable sales/CS, and launch data-driven releases that increase transparency and structure in healthcare pricing.

Act as a trusted technical advisor to payer and provider customers, translate data and reporting needs to engineering, provide analytics support and education, assist data migrations, design robust data structures with data engineering, and feed customer-driven product feedback to increase adoption.

Design, build, and maintain scalable data models and pipelines using Python and SQL. Collaborate with product and SMEs to create analytics-ready healthcare datasets, maintain data engineering systems, document solutions, and iterate based on product feedback.

Build and operate the platform: manage EKS/Kubernetes, implement GitOps (ArgoCD), maintain CI/CD (GitHub Actions), define SLIs/SLOs, lead incident response/on-call and post-mortems, design observability (Datadog, Sentry, CloudWatch, Grafana), automate infra with IaC (Terraform/CloudFormation/Crossplane), write Python tooling, and shape platform architecture and self-service workflows.

Produce short-form educational multimedia about healthcare pricing: plan and shoot video, capture audio, edit for clarity and platform, pitch creative formats, use AI tools, and collaborate with leadership and teams.

Responsible for sales and expansion at Turquoise Health, managing the full sales cycle with high-level executive stakeholders in healthcare.

The Engineering Manager for Data Products will lead a team of engineers in building data analysis products, ensuring high-quality delivery, and fostering collaboration in a remote work environment.

Lead end-to-end ownership of personalization platform surfaces (onboarding, preferences, notifications, reporting). Define systems-level primitives, turn ambiguous horizontal challenges into concrete requirements, use data and AI prototyping to validate, and coordinate engineering, design, data, and GTM to ship scalable B2B SaaS experiences.

Build and maintain analytics pipelines, data models, dashboards, and analyses to measure product metrics and business outcomes. Partner with Product, Engineering, and stakeholders to define metrics, evaluate initiatives, enable self-service analytics, and establish analytics best practices for scalable, performant data systems.