Role Description & Responsibilities
Data Engineering
About Figma
Figma is a high-growth technology leader building scalable solutions for global users. Millions of companies and teams rely on their infrastructure and services to execute high-value workflows, analyze massive datasets, and scale modern operations.
CadetX Candidate Advantage: As an applicant for this Data Engineering role, you are evaluated on practical proficiency with real-world query execution, data pipelines, and clear stakeholder communication.
CadetX Role Brief & Company Specifications
Role Mission & Impact
Figma is evolving the Product Support experience, powered by AI, automation, and integrated systems. The AI Infrastructure & Tooling team helps make that possible by building intelligent, resilient, and integrated solutions that automate workflows, connect systems, and streamline support operations. As a Support Engineer, AI Infrastructure & Tooling on this team, you'll be the technical execution layer that brings our support tools, customer and account context, internal systems, and AI workflows together.
You'll design, build, and operationalize integrations across systems like Decagon, Zendesk, Figma admin tooling, and adjacent Product Support platforms. Your work will help us bring the right context into customer conversations, automate complex workflows, and optimize both the customer and Specialist experience by applying AI where it can meaningfully improve support workflows, quality, and efficiency.
Key Responsibilities & Day-to-Day Scope
- ✓ Build and operationalize AI-powered workflows that improve Product Support experiences for customers and internal support teams
- ✓ Design and maintain integrations across Decagon, Zendesk, Figma admin tooling, internal data sources, and adjacent Product Support platforms
- ✓ Bring relevant customer, account, product, billing, file, or admin metadata into support conversations so chatbots and Specialists have the context they need to resolve issues more effectively
- ✓ Use LLMs and AI patterns for classification, summarization, routing, recommendations, context enrichment, and workflow automation
- ✓ Partner with Engineering, Analytics, Security, Programs, Support, and vendor teams to align on requirements, implementation, governance, and rollout
- ✓ Build quality checks, monitoring, fallback paths, and operational guardrails so AI-powered workflows can be trusted in production
- ✓ Define success metrics for each workflow, track adoption and impact, and iterate based on customer outcomes, Specialist efficiency, and adoption
- ✓ 3+ years of experience shipping integrations, automations, or internal tools across customer-facing operational systems
- ✓ Strong proficiency in modern back-end technologies and languages (e.g., Ruby, Python, Go, C++, PostgreSQL), with hands-on experience building APIs, implementing webhooks, orchestrating data flows, and integrating systems across complex workflows.
Official Qualifications & Technical Requirements
- • Hands-on experience with LLM-powered workflows, AI automations, or AI-enabled customer/support experiences, including working with operational data to debug issues, improve workflows, and measure impact
- • Strong product and stakeholder instincts: you can translate ambiguous support problems into practical, adopted, and measurable technical solutions
- • Proven track record of designing AI workflows with clear guardrails, fallback paths, and responsible deployment practices
- • Experience with support platforms like Zendesk, Decagon, Sprinklr, Gainsight, Maestro QA/Rippit, Assembled, Salesforce, or similar systems
- • Familiarity with agent assist tooling, AI support chatbots, copilot tooling, RAG, AI observability, or monitoring AI workflows in production
- • Experience building internal Slack tooling, workflow automations, or embedded support experiences
- • Background in Support Engineering, Internal Tools Engineering, Solutions Engineering, Support Operations, CX Systems, or Business Systems
- • Familiarity with customer support metrics such as containment, deflection, CSAT, first contact resolution, routing accuracy
Preferred Skills & Technology Stack
- + Experience with big data tools (Apache Spark, Kafka, Flink) and cloud platforms (AWS, Azure, GCP).
- + Familiarity with dbt (data build tool) for SQL transformations.
- + Hands-on experience with Docker and Linux system administration.
Tech Stack & Tools in this role
Expected technical competencies for Day 1 productivity:
★ Life & Culture at Figma
Verified EmployerFigma champions rigorous analytical discipline, continuous learning, and low-ego teamwork. Team members enjoy flexible hybrid allowances, learning stipends, and structured mentorship to fast-track career mobility.