Director of Connectors, Veeam

• 10+ years in backend or data engineering focused on integrations, ETL, or API-first systems, plus 4+ years leading engineering teams
• Proven experience architecting and shipping connectors or integration platforms for major SaaS and cloud ecosystems (AWS, Azure, Google Cloud, or complex enterprise apps) at scale
• Strong skills in Go and/or Python, especially for high-concurrency and high-throughput work
• Strong understanding of distributed systems, including microservices, REST/GraphQL APIs, and message brokers such as Kafka or RabbitMQ
• Solid data fundamentals across SQL and NoSQL (graph databases such as Neo4j or Neptune are a plus)
• Hands-on experience using AI-assisted development tools (Claude Code, Cursor, Copilot) and the judgement to help a team use them well; building reusable Claude skills or custom agents is a strong plus

Desirable

• You will own the Universal Connector Framework for Veeam Data Command Center and lead the team responsible for building and operating integrations across SaaS, IaaS, and AI platforms
• In this role, you will help maintain visibility and control as data and AI usage expands across more tools and environments. Your teams will build connectors that discover sensitive data, collect access and permission details, and monitor GenAI-related activity and deliver this information into the platform to support consistent visibility and governance
• You will set the technical strategy, define standards for scalability and reliability, and partner across product and engineering to prioritize the integration roadmap
• Own the connector framework by setting the architecture and roadmap for a scalable, flexible platform that supports integrations such as Snowflake, Salesforce, Databricks, Microsoft 365, and vector databases like Pinecone and Milvus
• Lead and grow the team by hiring, mentoring, and developing senior engineers, setting clear standards, and owning delivery, reliability, and scale
• Build an AI-first engineering practice where the team uses tools like Claude Code, Cursor, and Copilot with guardrails that protect code quality and security
• Create reusable “Claude skills” and workflows that standardize connector patterns, auth flows, and metadata normalization so each new integration is faster to build
• Improve AI-driven discovery by driving LLM-based detection for Shadow AI in third-party apps and building the telemetry ingestion needed for AISPM (AI Security Posture Management)
• Connect to the graph platform by ensuring connectors extract and normalize metadata, permissions (ACLs), and lineage into the centralized platform
• Deliver high-scale, resilient scanning by guiding distributed workers (Go and/or Python) that scan very large environments across hybrid cloud without disrupting source systems, including strong auth support (OAuth, mTLS, JWT) and self-healing behavior for rate limits and failures