Extend

• Serve as the escalation point of contact from outsourced partners, customer support teams and service technician partners. • Investigate and resolve complex claim situations, ensuring decisions align with contract terms and internal policies. • Troubleshoot delays in claims processing, ensuring timely resolutions and enhanced customer satisfaction. • Coordinate service technician dispatching and parts ordering when necessary to support claim resolution. • Resolve escalated customer issues by proposing fair resolutions within policy guidelines and clearly communicating outcomes to the customer. • Maintain detailed and organized documentation of all customer and partner interactions. • Investigate suspected fraudulent claims and report findings as needed. • Recommend process improvements and opportunities to automate routine tasks. • Provide timely responses to claim inquiries via phone and email, in line with established SLAs. • Collaborate with Product and Engineering teams to troubleshoot and escalate unresolved claim issues. • Stay up to date on product offerings, service workflows, and policy updates to better support customers. • Identify and communicate training needs for front-line support teams to improve service quality. • Keep internal systems current with relevant technical issues, customer feedback, and insights.

• Create polished, dev-ready Figma design deliverables for end-to-end experiences. • Take ownership of assigned product features and user flows, translating high-level requirements into intuitive, functional visual design deliverables. • Partner closely with Product Managers, Engineering and stakeholders to deliver persona-first, data-driven solutions. • Leverage user research, analytics, and feedback to iterate on designs. • Leverage modern AI-assisted tools to automate repetitive tasks and rapidly prototype high-fidelity explorations. • Actively use and contribute to the Extend Design System. • Participate in design reviews and critiques, providing constructive feedback to peers.

• Lead post-sales technical implementation conversations with merchants, conducting deep technical discovery to understand their systems, integration architecture, and constraints — and translating findings into a clear path forward. • Own the technical onboarding experience, serving as the primary technical point of contact from kickoff to go-live, partnering with internal Engineering and Product teams to identify and address any product gaps and ensure integrations are set up for long-term success. • Establish and maintain technical documentation for merchant onboardings — system architecture diagrams, API inventories, and integration specs — that cross-functional partners rely on throughout the merchant lifecycle. • Build and maintain a library of reusable integration assets — templates, configuration guides, and onboarding playbooks — that improve consistency and reduce time-to-live across the merchant portfolio on the Solutions team. • Lead investigation and resolution of complex post-onboarding technical issues, collaborating with Engineering and Support to drive root cause analysis and communicate findings clearly to internal and external stakeholders. • Own pre-launch QA — including sandbox testing and end-to-end validation of integration flows — ensuring integrations meet a high technical bar before go-live. • Identify and champion AI and automation opportunities that improve operational efficiency, partnering with internal teams to move initiatives from idea to execution.

• Tackle ambiguous, cross-system problems spanning all aspects of fulfillment and servicing • Own large features end to end, from design through deployment, release, and monitoring • Deliver scalable, event-driven, AI-native applications that elevate the customer experience • Collaborate with platform teams to enhance overall system architecture • Own the design, implementation, quality and safety of AI/agentic features in production systems • Mentor other engineers across levels through code review, pairing, design collaboration and coaching • Drive adoption of tooling and practices, including AI developer tooling, and codify the habits that make them effective • Advocate for the proactive investments — refactors, observability, upgrades, technology adoption - that enable continuous improvement • Communicate fluently across product, design, solutions, and ops to align outcomes with business goals

• Own the model lifecycle: requirements, experimentation, model development, evaluation, and model cards, partnering with ML engineers on deployment and production infrastructure • Translate complex fraud patterns into well-framed ML solutions: defining what to model, what success looks like, and where ML adds value vs. simpler approaches • Design and maintain feature engineering pipelines for model development • Monitor model quality in production, tracking performance over time, detecting data drift, and determining when to retrain • Partner closely with leadership, go-to-market, fraud operations, product, and engineering teams to define and execute effective fraud strategies • Champion a culture of continuous learning, experimentation, and collaboration across the fraud and broader data science teams

• You run intake from across the company, prioritize against business value and technical readiness, and keep the queue of business-system connectors moving. • You lead exec-prioritized cross-functional projects from intake through ship. • You triage requests, manage office hours, and run the AI Builds and champion support channels. • You hold the map of who owns what across DevX, DevOps, Product Engineering, and the network of Champions in the business. • You run the planning, check-in, and reflect cadences.

• Price deals no one has priced before. • Turn a monitoring function into a decision engine. • Own the risk narrative with senior leadership. • Build the data foundation for Risk Analytics — and make it yours. • Actually use AI to change how the team works.

• Design and ship secure MCP (Model Context Protocol) connectors to Extend's internal systems and the third-party SaaS we run on: finance, CRM, data warehouse, expense management, product analytics, support, ATS, and the long tail beyond. • Build and curate the shared library of agent skills that every team at Extend composes from. Ship skills, codify patterns, and raise the floor for what a safe, high-quality skill looks like. • Extend our agent infrastructure. Build the tooling that lets non-engineers create reusable agent skills securely and reliably. Encode the review and publishing model for shared tooling, shared runtimes, and the feedback loop on agent behavior in production. Fill the open phases of the lifecycle that governs how skills are designed, reviewed, and shipped, so non-engineers can build and ship intelligent automation end to end. • Build toward a connector-building agent: a meta-agent that discovers APIs, scaffolds MCP servers, and provisions access automatically. The end state is a platform that is itself an agent. • Work with our platform teams to establish the credential scoping, OpenTelemetry instrumentation, and least-privilege patterns that every connector and skill ships with, so security is built in from day one. • Own the employee experience for the agentic platform. Help onboard employees to the tools with self-serve guides, build skills people can learn from, and run the feedback loop between what's shipped and what adopters actually need. Your job isn't done when the connector ships. It's done when the team using it is self-sufficient. • Design credential scoping and vending for agent connectors: how API keys are provisioned, rotated, and scoped per user, per skill, per connector. OAuth/OIDC where it fits, least-privilege everywhere. • Build the risk-tier and review model for shared agent skills: what's safe at personal, team, and org level; sandboxing strategy; malicious dependency scanning for skills that pull in untrusted packages. • Instrument the agent platform end-to-end with OpenTelemetry: every MCP call, every skill execution, every credential use is visible in Coralogix.

• Own the model lifecycle: requirements, experimentation, model development, evaluation, and model cards, partnering with ML engineers on deployment and production infrastructure • Translate business problems into well-framed ML solutions: defining what to model, what success looks like, and where ML adds value vs. simpler approaches • Design and maintain feature engineering pipelines for model development • Drive experiment design and statistical rigor: ensuring models are evaluated with sound methodology before and after launch • Monitor model quality in production, tracking performance over time, detecting data drift, and determining when to retrain • Cultivate a culture of learning and collaboration within and across partner teams • Perform design and code reviews to raise the technical excellence bar • Hire, mentor, and coach data scientists

• You own our data warehouse and the reporting layer on top of it, setting patterns for how data is modeled, evolved, and exposed. • You write SQL and dbt models, refactor transformations, and build the tables and views downstream teams rely on. • You proactively engage with teams across the company to understand how data is created and used, identify gaps, and guide solutions. • You partner with our DevX and architecture teams on the boundary between product engineering services and Snowflake. • You build models, tests, and processes that anticipate malformed data and upstream changes, making our pipelines boring to operate. • You instrument what you own, define meaningful SLOs and data quality checks, and participate in our rotating on-call schedule. • You own and extend our Python jobs running on Glue, Lambda, and Step Functions. • You pair with more junior engineers on real work, raise the bar on PR and architecture reviews, and define the patterns and standards the team writes against.