• Build and maintain backend features - APIs, background jobs, integrations. Our stack is Ruby on Rails but if you're strong in Python or TypeScript and willing to learn, that works too. • Improve and maintain our data pipeline - write reports and queries in PostgreSQL and BigQuery to keep affiliate data (clicks, conversions, commissions, payouts) correct and up to date. • Build internal dashboards and reports - payout summaries, revenue attribution, cohort breakdowns, etc. • Fix performance issues - bad queries, slow background jobs, bottlenecks. Keep things running well under load. • Use AI tools (Claude Code, Cursor) daily to move faster. We care about what you ship, not how many hours you sit. • Work async with a small team - few meetings, lots of ownership. You ship it, you monitor it. • Jump in on technical support. We rotate who handles deeper technical help for our CX team - running custom scripts, debugging client-specific issues, or digging into the data when something looks off.

• Operate as a product engineer focused on solving real user and business problems, not clearing a backlog. You bring ideas, challenge assumptions, and own outcomes. • Work in an AI-first development flow. Use tools like OpenAI Codex and GitHub Copilot not just to go faster, but to do more: explore multiple approaches, validate ideas quickly, and build things that previously wouldn't have been worth the effort. • Primarily focused on backend: designing, developing, and maintaining scalable services with .NET 10, ASP.NET Core, and GraphQL. Comfortable stepping into front-end work when the problem calls for it, since AI-assisted development means you don't need to be a specialist to ship quality UI. • You're aligned to a product team and typically work alongside one or two other engineers on any given project, pairing on tough problems, reviewing each other's work, and shipping together rather than in parallel. • Uphold a high quality bar. Engineers write their own tests at the unit, integration, and end-to-end levels, and every line of code, human-written or AI-assisted, is reviewed by a human before it ships. • Drive technical improvements across the codebase. Proactively identify friction, propose changes, and see them through to adoption. Be a first responder for production. Use DataDog, Grafana, OpenTelemetry, and Microsoft Azure to quickly diagnose incidents, minimize impact, and harden the system against recurrence.

• Analyze, design, and implement features. • Develop Microservice Oriented Architecture & build reusable components. • Write well-designed, efficient, and testable code — using AI coding tools to accelerate implementation while maintaining code quality standards and performing unit and integration tests. • Analyze performance and reliability to ensure that data is moving fast and accurately. • Perform identification of code metrics and necessary analysis (code, requirements, system risk, and software reliability). • Correct anomalies and problems as they arise. • Test the implemented features to make sure all acceptance criteria are met. • Review code and provide guidance to other engineers to ensure that software is developed with maintainability, performance and security in mind. • Participate in projects from the initial idea to launch. • Advise the product owner on technological choices and decisions concerning developments. • Contribute to the continuous improvement of development velocity — including AI-assisted testing, automated deployment pipelines, and LLM-augmented code review practices. • Review the security/reliability implications of software design decisions. • Collaborate with the security team to integrate security best practices into software development processes. • Communicate risks associated to any activity, technology or processes as you identify them. • Participate in incident response activities and work with the infrastructure and security teams to investigate and remediate incidents.

• Lead the architectural design and technical roadmap for scalable, high-performance data processing pipelines capable of handling petabyte-scale telemetry data (logs, metrics, traces). • Drive the development and optimization of ML-driven data routing and transformation engines to reduce customer data volumes by 80%+ while architecting real-time analytics systems using advanced machine learning and LLMs. • Design cloud-native microservices and APIs that integrate seamlessly with major observability platforms (Splunk, Elastic, Datadog) while establishing robust monitoring, alerting, and observability solutions. • Lead cross-functional technical initiatives and decision-making forums, collaborating with Product, Data Science, and DevOps teams to translate strategic vision into technical solutions and company-wide standards. • Provide technical leadership and mentorship to senior and junior engineers, driving system performance and reliability optimization while establishing engineering best practices and culture.