Engineer V - Software

Job Summary

As a Software Engineer V, you set technical direction. You own architecture that spans teams — designing the systems others build within — and you are accountable for technical outcomes delivered through others as well as by your own hands. You define the problems as much as you solve them: turning ambiguous business goals into technical strategy, sequencing multi-quarter initiatives, and making the build-vs-buy and migration calls that shape the platform for years.

You are still hands-on. You prototype the risky parts, write the critical-path code, and stay close enough to production reality that your designs survive contact with it. And you shape how we test and learn at scale — building platforms where experimentation is cheap, so every team can iterate on price display, communications, chat, and beyond with real customer data.

A Day in the Life

• Technical Direction: Own the target architecture for a platform or domain that spans multiple teams. Author and drive the technical proposals and design reviews that align engineers around it.
• Force Multiplication: Mentor senior engineers, not just early-career ones. Raise the bar org-wide through reusable patterns, standards, and incisive design review.
• Hands-On Delivery: Prototype novel or high-risk components and ship critical-path code. Your credibility comes from working software, not slides.
• Operational Excellence: Define SLOs and the reliability strategy for your domain. Lead retrospectives on major incidents and turn them into architectural improvements.
• Test and Learn at Scale: Shape the experimentation strategy for your domain and build systems that make running a test a config change, not a project.
• Momentum: We work in short cycles, code released in days, experiments read out weekly, priorities adjusting as results come in. You design the architecture that makes this speed safe — systems where shipping fast and shipping well are the same motion.
• Strategic Partnership: Work with Product, leadership, and across organizations to align technical strategy with the business roadmap; make and defend build/buy/migrate decisions.

Responsibilities

• Architecture Ownership: Design and evolve systems spanning team boundaries; define the integration contracts and domain boundaries other teams build against.
• Migration Leadership: Plan and lead incremental, zero-downtime migrations off legacy systems while the business keeps running.
• Technical Risk Management: Identify the riskiest assumptions in a multi-quarter initiative early, and design spikes and milestones that retire them fast.
• Engineering Standards: Own key parts of the software design process, review culture, and production-readiness bar across your domain.
• Hiring Bar: Help design interview loops and calibrate the technical bar for senior hires.

Our Tech Stack

• Front-end: React, Vite, Next.js, TypeScript.
• Back-end: Kotlin, Spring Boot, PostgreSQL.
• Cloud & Infrastructure: Microsoft Azure (Kubernetes, Functions, Front Door), Docker, Helm.
• Data & Analytics: Snowflake, Salesforce CDP.
• Experimentation & Observability: Eppo, Datadog.
• DevOps: Azure DevOps (ADO), Pipelines.
• AI Tooling: Cursor, Claude.

Qualifications

Basic Qualifications

• Education: Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent experience).
• Experience: 8+ years of professional software engineering experience, including leading multi-quarter technical initiatives that spanned multiple teams.
• Distributed Systems: A track record of designing, operating, and evolving distributed systems in production at meaningful scale.
• Cloud Architecture: Deep expertise in cloud-native architecture (Azure preferred): microservices, eventing, API design, and the failure modes of each.
• Design Communication: Exceptional written design skills — you can align a dozen engineers through a document.

Preferred Qualifications

• Experience as the founding or lead architect of a platform other teams build on.
• Experience with data-warehouse-native experimentation (Snowflake) and observability at scale (Datadog).
• Fluency with AI-assisted development workflows (e.g., Cursor).