Senior Backend Engineer, Connectors & Discover

Zendesk Zendesk · Enterprise · San Francisco, CA +4

Senior Backend Engineer responsible for the data layer powering Zendesk's AI products, focusing on building and scaling integrations and analytics infrastructure to transform raw support data into automation recommendations. This involves designing and maintaining connectors (ETL), extending the insights engine, building resilient data pipelines, and optimizing search indices for RAG and retrieval features.

What you'd actually do

  1. Design, build, and maintain robust platform connectors (ETL: Client → Extractor → Transformer → Loader) across third‑party systems, handling auth, pagination, rate limits, and schema drift.
  2. Extend and scale the Discover insights engine: clustering topics, detecting knowledge gaps, calculating deflection/opportunity impact, and surfacing automation recommendations.
  3. Build and operate resilient data pipelines: real-time webhooks, incremental sync, reindexing, and metrics aggregation with worker queues.
  4. Design, optimize, and maintain search indices and aggregation pipelines for full-text, vector, and hybrid search at scale.
  5. Collaborate with product, ML, and design to turn analytics outputs into reliable, explainable customer-facing metrics and recommendations.

Skills

Required

  • Python
  • FastAPI
  • MongoDB
  • Elasticsearch/OpenSearch
  • Redis
  • Distributed job processing (RQ, Celery, or similar)
  • Backend engineering
  • Data pipelines
  • Integration platforms
  • Search infrastructure
  • Analytics infrastructure
  • RAG
  • Retrieval features
  • Distributed systems
  • Multi-tenant isolation
  • Webhook security
  • Incremental sync correctness
  • Fault-tolerant processing

Nice to have

  • ETL/orchestration tools (Dagster, Airflow)
  • S3-based storage patterns
  • NLP pipelines
  • Embeddings
  • Production RAG systems
  • System design for multi-tenant SaaS
  • Webhook security (HMAC/OAuth)
  • Analytics engines
  • Product recommendations

What the JD emphasized

  • AI products
  • automation recommendations
  • customer outcomes
  • automation recommendations
  • RAG
  • retrieval features
  • NLP pipelines
  • embeddings
  • RAG systems

Other signals

  • AI products
  • automation recommendations
  • customer outcomes
  • automation recommendations
  • RAG
  • retrieval features
  • NLP pipelines
  • embeddings
  • RAG systems