• Collaborate with engineering, data science, ML, data engineering, and product analytics teams to understand and shape the future needs of our data platform and infrastructure. • Define, drive, and implement the future live ingestion layer of data into our data platform (e.g. Kafka, Kinesis). • Define and evolve standards for storage, compute, data management, provenance, and orchestration. • Monitor current and forecast future infrastructure needs and costs based on business requirements. • Implement and manage role-based access management. • Design and implement reusable tooling and frameworks for data consumers (e.g., data quality monitoring frameworks, templated pipelines, foolproof backfilling capabilities).

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We’re looking for a hands-on Senior Platform Engineer with strong experience operating production cloud systems. You’ll play a critical role in evolving our platform maturity — improving reliability, scalability, developer experience, and security across a growing SaaS environment. You will act as a technical leader within Platform Engineering, reporting to the Platform Engineering Manager. This role is well suited for someone who values continuous improvement, enjoys enabling others, and takes pride in making complex infrastructure simple, secure, and reliable. What You'll Be Expected To Own - Design and evolve our Azure architecture while supporting our legacy AWS footprint - Production ownership of Kubernetes (AKS) clusters, including upgrades, scaling, ingress, and secret management - Infrastructure as Code using Terraform across multiple environments - Secure, reliable CI/CD platforms for infrastructure, Kubernetes workloads, serverless services, and database deployments - Azure and AWS networking design (VNet architecture, segmentation, private endpoints, traffic flow) - Developer enablement through templates, documentation, and self-service tooling - Observability: metrics, logs, tracing, and actionable alerting - Secure access patterns including RBAC, managed identities, and database access controls - Production incident response and root cause analysis - Define and improve service reliability standards, including SLOs, SLAs, and resilience design patterns - SOC audit readiness and secure-by-design practices - Reducing technical debt and shifting from reactive firefighting to proactive design - Participate in an on-call rotation Qualifications - 6+ years in cloud infrastructure engineering (DevOps, Cloud Architecture, SRE), including at least 2 years in a Platform Engineer capacity - 2+ years hands-on Azure production experience including compute, storage, networking and serverless services - Experience working in a SaaS product company - Advanced proficiency in Kubernetes and containerization (Docker, Helm), including managing and optimizing production clusters - Proficiency in Infrastructure as Code (Terraform preferred) - Experience designing and owning end-to-end CI/CD platforms (Azure DevOps and/or GitHub), with GitOps mastery - Experience implementing monitoring, logging, alerting, and tracing solutions using Azure-native tools and OpenTelemetry - Proven production incident response and leading troubleshooting efforts - Experience improving developer productivity through internal tooling and automation - Strong proficiency in at least one scripting/programming language (e.g., Python, Bash) - Experience managing production databases (performance, scaling, backups, access control, resilience) - Strong understanding of cloud security best practices and operating in regulated environments (SOC, fintech, payroll, healthcare) - A systems thinker accountable for production systems, collaborative in approach, and focused on enabling teams through automation and modern tooling Bonus Points If - Azure and/or AWS certifications - DevSecOps or SRE experience Benefits - Impact: Roll up your sleeves and directly contribute to the growth and success of Wagepoint by shaping our workforce - Culture: The opportunity to work with the world’s friendliest team, solving interesting problems together with an endless amount of laughter - Growth: Opportunities for professional development and career advancement - we are always, always learning with a growth oriented mindset - Innovation: Work in an environment that encourages creativity and new ideas - Remote: The ability to work from home, forever! Wagepoint is a remote company Next Steps If all the above feels right and proper, please don’t feel like you need to send us an uninspired cover letter. Just send us your updated resume and perhaps a few lines on what excites you about the job - we really want to get to know who you are, not just about how we can work together. If we believe you’re a good match, someone from our team will reach out to schedule a call. Psssst! Wagepoint is an equal opportunity employer. We welcome and appreciate all applicants with diverse backgrounds, races, religions, genders, identities, ages, experiences. We are committed to making not only our workplace safe and inclusive for everyone, but also ensuring our recruitment process reflects that.

• Design and build the internal foundation that powers development lifecycle • Build internal developer tools and self-service portals for AWS resource provisioning • Lead transition toward modular, reusable IaC modules • Architect robust delivery pipelines with automated security scanning • Define and enforce organization-wide standards for microservices • Build observability-as-a-service layer for new services • Consult with product engineering teams on service architecture optimization

• Design, prototype and productionize scalable AI/machine learning models • Play a critical in setting up the best practices in machine learning, setting direction of the machine learning platform • Develop frameworks, pipelines, libraries, utilities and tools that process massive data for ML tasks • Partner with ML engineers and data scientists to troubleshoot and optimize complex ML pipelines • Work with product managers and business partners to gather requirements for machine learning models • Build model deployment platform that can simplify implementing new models • Build end-to-end reusable pipelines from data acquisition to model output delivery • Mentor and guide junior data scientists to deploy their models into production • Design & Build ML (engineering) solutions that unlock new ML modeling capabilities for BetterHelp