Senior Search Engineer - OpenSearch

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Software EngineeringSearch Engineer

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Overview

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Search Engineer - OpenSearch based in Brazil.

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Software EngineeringSearch Engineer

This role focuses on building advanced search capabilities that help users quickly find materials, customers, orders, and documents across massive enterprise datasets.
You will play a key role in designing and optimizing search infrastructure built around OpenSearch and modern relevance techniques.
The position combines product engineering with hands-on delivery, allowing you to shape platform capabilities while solving customer-specific search challenges.
You will work across index and schema design, ingestion pipelines, query optimization, relevance engineering, and search APIs.
A major focus will be applying strong technical judgment to improve search quality, performance, scalability, and reliability.
You will collaborate with product, implementation, solution engineering, IT, and customer teams in a technically diverse environment.
The role is ideal for a senior search specialist who enjoys solving complex search problems and turning measurable improvements into production-ready capabilities.

Design index mappings, analyzers, and sharding strategies suited to large, high-cardinality enterprise catalogs.
Build and maintain indexing pipelines that synchronize search data with SAP source systems, including full reindexing and incremental update processes.
Design and optimize queries using query DSL, scoring and boosting, synonyms, stemming, fuzzy and typo tolerance, faceting, and aggregations.
Develop and apply relevance measurement approaches to demonstrate whether search improvements produce measurable gains.
Support customer delivery engagements by profiling catalogs, tuning index and query configurations, and addressing customer-specific relevance requirements.
Diagnose and resolve search performance issues involving expensive queries, mapping and analyzer design, sharding decisions, and data modeling.
Implement vector and hybrid search alongside lexical search when semantic matching can measurably improve results.
Define platform requirements such as cluster sizing, configuration, index lifecycle policies, snapshot strategies, and upgrade requirements.
Build clean, reliable, and well-documented search APIs for a TypeScript-based product stack.
Define search health, quality, and performance signals that the platform should monitor and surface.
Collaborate with product management on search roadmap priorities and with solution engineering on multi-tenant architecture considerations.
Document search architecture and relevance decisions so engineering teams can understand and maintain the underlying design.
Review search-related work from teammates and contribute to higher standards for query design, relevance engineering, and technical rigor.

Requirements

~2 min read

Deep hands-on expertise with OpenSearch or Elasticsearch, ideally including production search systems that you have personally designed and built.
Strong command of query DSL, mappings, analyzers, aggregations, and the various mechanisms that influence search relevance.
Proven experience in relevance engineering, including the ability to demonstrate and measure improvements in search quality.
Strong understanding of how index and cluster architecture affects performance, including sharding, mapping design, query cost, and Lucene fundamentals.
Excellent debugging skills for slow or expensive queries, mapping and analyzer issues, and search results that fail to meet user expectations.
Experience building high-throughput ingestion and indexing pipelines connected to systems of record.
Proficiency in TypeScript, JavaScript, and/or Python for developing services, pipelines, and search tooling.
Comfortable working directly with customers and delivery teams to understand requirements and solve search problems.
Experience collaborating with separate platform or IT teams by clearly specifying requirements, handing over solutions, and participating in technical diagnosis.
Strong written and verbal communication skills, with a habit of documenting technical decisions and relevance trade-offs.
Bachelor's degree in Computer Science or a related technical field, or equivalent practical experience.
Experience with vector and semantic search, including embeddings, OpenSearch k-NN, hybrid ranking, or learning-to-rank, is preferred.
Familiarity with OpenSearch Dashboards, Data Prepper, Logstash, or Kafka-based ingestion is a plus.
Experience working with SAP or ERP material master and catalog data, including part numbers, cross-references, and unit-of-measure complexity, is advantageous.
Knowledge of query understanding techniques such as entity extraction, intent classification, spell correction, and autocomplete is a plus.
Experience designing multi-tenant search architectures and tenant data isolation is preferred.
Customer-facing implementation, delivery, or professional services experience is advantageous.
Working knowledge of containers and Kubernetes is a plus.
Contributions to OpenSearch, Lucene, or other open-source search projects are valued.