Senior Search Engineer – OpenSearch

DXS - Direct Expansion Solutions

. Design and build search capabilities for the OMS+ platform .

Posted 10/2/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates deep expertise in OpenSearch or Elasticsearch, with a focus on designing and building search capabilities, indexing pipelines, and query optimization. Proficient in TypeScript and Python, with strong communication skills for customer collaboration and documentation.

Highest-signal resume keywords

OpenSearch ExpertiseQuery DSL ProficiencyRelevance EngineeringIndex and Cluster DesignHigh-Throughput Ingestion

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

OpenSearchElasticsearchQuery DSLMappingsAnalyzersAggregationsIndex DesignShardingTypeScriptPython

Soft Skills

Clear CommunicationCollaborationDebugging AbilityCustomer Engagement

Tools & Technologies

OpenSearch DashboardsData PrepperLogstashKafkaContainersKubernetes

Certifications & Qualifications

Bachelor's Degree in Computer Science

Industry Keywords

Search QualityRelevance MeasurementMulti-Tenant ArchitectureSAPERP

Tech Stack

Tools & technologies

ElasticSearchERPJavaScriptKafkaKubernetesLogstashPythonTypeScript

About the role

Key responsibilities & impact

Design and build search capabilities for the OMS+ platform
Design index mappings, analyzers, and sharding strategies for large, high-cardinality enterprise catalogs
Build and maintain indexing pipelines synchronized with SAP source systems, including full reindex and incremental update paths
Design and tune queries, including query DSL, scoring and boosting, synonyms, stemming, fuzzy and typo tolerance, faceting, and aggregations
Contribute to relevance measurement and prove improvements in search quality
Profile customer catalogs and tune index and query design for customer environments
Work with implementation and solution engineering teams on customer-specific relevance requirements
Diagnose and resolve search performance problems involving expensive queries, mappings, analyzers, sharding, and data modeling
Implement vector and hybrid search alongside lexical search where semantic matching improves results
Specify platform needs including cluster sizing, configuration, index lifecycle policies, snapshots, and upgrades
Build documented search APIs for the TypeScript product stack
Define search health and quality signals surfaced by the platform
Collaborate with product management on roadmap search priorities and solution engineering on multi-tenant considerations
Document search architecture and relevance decisions
Review teammates' search-related work and raise standards for query design and relevance rigor

Requirements

What you’ll need

Deep, hands-on OpenSearch or Elasticsearch expertise, ideally with production search designed and built independently
Command of query DSL, mappings, analyzers, and aggregations
Proven relevance engineering with measurable search-quality improvements
Understanding of index and cluster design, sharding, mapping design, query cost, and Lucene fundamentals
Strong debugging ability for slow or expensive queries, mapping and analyzer mistakes, and poor search results
Experience building high-throughput ingestion and indexing pipelines against systems of record
Proficiency in TypeScript/JavaScript and/or Python
Comfort working directly with customers and delivery teams
Ability to collaborate with a separate platform or IT team on requirements and diagnosis
Clear communication and documentation of decisions and relevance trade-offs
Bachelor's degree in Computer Science or a related technical field, or equivalent practical experience
Resume must be submitted in English
Preferred: vector and semantic search, embeddings, OpenSearch k-NN, hybrid ranking, or learning-to-rank
Preferred: OpenSearch Dashboards, Data Prepper, Logstash, or Kafka-based ingestion
Preferred: SAP or ERP material master and catalog data experience
Preferred: query understanding, entity extraction, intent classification, spell correction, or autocomplete
Preferred: multi-tenant search architecture and tenant data isolation
Preferred: customer-facing implementation, delivery, or professional services experience
Preferred: containers and Kubernetes
Preferred: contributions to OpenSearch, Lucene, or other open-source search projects

Benefits

Comp & perks

Full-time contractor arrangement
Equal employment opportunity and affirmative-action employer protections