Senior Backend Engineer

Role Overview

Join Zendesk's mission to power exceptional service for every person on the planet as a Senior Backend Engineer, Connectors & Discover. This role focuses on owning and scaling the data layer that fuels our AI products.

Key Responsibilities:

• Data Integration Architecture: Design, build, and maintain robust platform connectors (ETL: Client → Extractor → Transformer → Loader) across third-party systems. You will handle complex challenges including authentication management, pagination handling, rate limiting strategies, and schema drift mitigation.
• The Discover Insights Engine: Extend and scale our insights engine by clustering topics, detecting knowledge gaps, calculating deflection/opportunity impact metrics, and surfacing actionable automation recommendations to users.
• Pipeline Resilience & Operations: Build and operate resilient data pipelines supporting real-time webhooks, incremental synchronization strategies, reindexing processes, and comprehensive metrics aggregation using worker queues.
• Search Infrastructure Optimization: Design, optimize, and maintain high-performance search indices and aggregation pipelines capable of handling full-text, vector, and hybrid search at scale.
• Distributed Systems Engineering: Solve critical distributed systems problems around multi-tenant isolation strategies, webhook security protocols (HMAC/OAuth), incremental sync correctness guarantees, and fault-tolerant processing architectures.
• Cross-Functional Collaboration: Work closely with product managers, machine learning engineers, and design teams to transform analytics outputs into reliable, explainable customer-facing metrics and recommendations that deliver measurable ROI.

Must-Have Qualifications:

• Experience Level: 3+ years of professional backend engineering experience, ideally within the B2B SaaS sector.
• Languages & Frameworks: Proficiency in Python and modern backend frameworks (specifically FastAPI).
• Databases & Caching: Production-grade experience with MongoDB, Elasticsearch/OpenSearch, and Redis.
• Job Processing: Deep expertise designing and operating distributed job processing systems using tools like RQ or Celery.
• Mindset: Strong ownership instincts to take systems from prototype concepts through to production-grade reliability.
• Data Engineering: Practical knowledge of building data pipelines or integration platforms with a relentless focus on correctness and observability.
• RAG & Analytics: Experience with search/analytics infrastructure powering Retrieval-Augmented Generation (RAG) and retrieval features, including NLP pipelines and embeddings production systems.

Preferred Qualifications:

• Familiarity with ETL/orchestration tools such as Dagster or Airflow alongside S3-based storage patterns.
• Prior work on analytics engines that effectively translate raw metrics into actionable product recommendations.
• System design experience for multi-tenant SaaS environments and webhook security implementation (HMAC/OAuth).