Senior Software Engineer, Connectors & Discover

Zendesk•Madison, WI

2d•$145,000 - $217,000•Hybrid

About The Position

Zendesk is seeking a Senior Backend Engineer to own the data layer powering its AI products. The role builds and scales integrations and analytics infrastructure that transform raw support data into trusted automation recommendations, helping customers reduce support costs and improve outcomes. The engineer will develop platform connectors across third-party systems and extend the Discover insights engine. They will build resilient data pipelines, operate search and aggregation infrastructure for full-text, vector, and hybrid search, and solve distributed-systems challenges involving multi-tenant isolation, webhook security, synchronization correctness, and fault-tolerant processing. The role collaborates with product, machine learning, design, data science, and customer-facing teams to deliver reliable, explainable metrics and recommendations. Zendesk describes its working approach as hybrid: employees come together in person at an office to connect, collaborate, and learn, while retaining flexibility to work remotely for part of the week. The US annualized base salary range is $145,000 to $217,000. The position may also be eligible for bonus, benefits, or related incentives; the listed salary range reflects base salary only.

Requirements

At least 3 years of professional backend engineering experience, ideally in B2B SaaS.
Proficiency in Python and backend frameworks; Zendesk uses FastAPI.
Production experience with MongoDB, Elasticsearch or OpenSearch, and Redis.
Experience designing and operating distributed job processing, using RQ, Celery, or similar.
Strong backend engineering skills and an ownership mindset, including taking 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.
Ability to collaborate with product, data science, and customer-facing teams to deliver measurable ROI.

Nice To Haves

Experience with ETL or orchestration tools such as Dagster or Airflow, and S3-based storage patterns.
Familiarity with NLP pipelines, embeddings, or production RAG systems.
Experience designing multi-tenant SaaS systems and securing webhooks using HMAC or OAuth.
Prior work on analytics engines that translate metrics into product recommendations.

Responsibilities

Design, build, and maintain platform ETL connectors across third-party systems, handling authentication, pagination, rate limits, and schema drift.
Extend and scale the Discover insights engine, including topic clustering, knowledge-gap detection, deflection and opportunity impact calculations, and automation recommendations.
Build and operate resilient data pipelines for real-time webhooks, incremental synchronization, reindexing, and metrics aggregation using worker queues.
Design, optimize, and maintain search indices and aggregation pipelines for full-text, vector, and hybrid search at scale.
Address distributed-systems challenges involving multi-tenant isolation, webhook security, incremental synchronization correctness, and fault-tolerant processing.
Collaborate with product, machine learning, and design teams to turn analytics outputs into reliable, explainable customer-facing metrics and recommendations.