Senior Backend Engineer, Connectors & Discover

Zendesk•Madison, WI

1d•Hybrid

About The Position

Join us at Zendesk, where we're on a mission to power exceptional service for every person on the planet. We're accelerating that ambition by building products rooted in AI, automation, and intelligent customer experiences, because behind every interaction lies an opportunity to make a human connection. We’re seeking a Senior Backend Engineer to own the data layer that powers Zendesk’s AI products. In this role, you’ll build and scale integrations and analytics infrastructure that transform raw support data into trusted automation recommendations — directly enabling customers to reduce support costs and improve customer outcomes.

Requirements

Strong backend engineering instincts and ownership mindset; you take systems from prototype to production-grade.
Deep experience building data pipelines or integration platforms with attention to correctness and observability.
Practical knowledge of search and analytics infrastructure powering RAG and retrieval features.
Experience with distributed job processing patterns and worker queue design.
A collaborative approach for working with product, data science, and customer-facing teams to deliver measurable ROI.
3+ years professional backend engineering experience, ideally in B2B SaaS.
Proficient in Python and backend frameworks (we use FastAPI).
Production experience with MongoDB, Elasticsearch/OpenSearch, and Redis.
Experience designing and operating distributed job processing (RQ, Celery, or similar).

Nice To Haves

Experience with ETL/orchestration tools (Dagster, Airflow) and S3-based storage patterns.
Familiarity with NLP pipelines, embeddings, or production RAG systems.
Experience with system design for multi-tenant SaaS and webhook security (HMAC/OAuth).
Prior work on analytics engines that translate metrics into product recommendations.

Responsibilities

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