We are looking for a highly skilled Elasticsearch Expert who can architect, optimize, and evolve our entire search ecosystem. This person will own how search works across millions of products, ensure high-performance indexing and querying, design relevance algorithms, and make sure we utilize Elasticsearch to its maximum capabilities.
You will work closely with Engineering, Data, DevOps and Product teams to ensure our search infrastructure is scalable, resilient, cost-efficient, and delivers the best shopping experience.
Key Responsibilities 1️⃣ Search Architecture Ownership Design, build, and optimize Elasticsearch clusters for high availability, low latency, and large-scale indexing. Define index mappings, templates, analyzers, normalizers, tokenizers and custom pipelines suitable for multilingual, e-commerce data. Architect multi-index strategies (aliases, versioned indices, reindexing flows, rollover, hot-warm tiers). Implement proper sharding, replication, and routing strategies.
3️⃣ Advanced Query Engineering Build dynamic filtering systems (sizes, colors, categories, brands, attributes). Create complex real-time aggregations for availability, pricing, and insights. Implement vector search for embeddings (OpenAI or AWS-based semantic search). Optimize keyword search, fuzzy matching, synonyms, stemming rules, stop-words.
4️⃣ Performance & Scalability Optimize indexing throughput and speed for millions of documents. Solve cluster bottlenecks: heap usage, GC tuning, threadpools, cache layers. Manage ILM (Index Lifecycle Management) for hot/warm/cold architecture. Design highly available clusters with fault tolerance.
5️⃣ Observability & Maintenance Implement rich monitoring dashboards (Grafana, Kibana, CloudWatch). Setup alerts for cluster health, indexing failures, slow queries, JVM pressure. Perform periodic audits to ensure the cluster remains efficient and stable.
6️⃣ Data Pipeline & Integration Work with backend teams to design efficient indexing pipelines (queue-based or batch). Ensure clean, structured, normalized data models for indexing. Build fallback strategies for partial failures and safe reindexing rollout.
7️⃣ Cost Optimization & Best Practices Continuously monitor cluster costs and implement optimization: Hot vs Warm storage Shard count reduction Compression techniques Scaling policies Recommend cluster upgrades or migration improvements.
Bonus Skills Experience building search for e-commerce platforms. Experience with semantic search, embeddings, LLM integrations. Experience with A/B testing of ranking algorithms. Strong data-modelling experience for multi-attribute product catalogs.
Familiarity with log search, fraud signals search, personalization engines.
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