Asks for

The ad says“Command of the query DSL, mappings, analyzers, and aggregations, and a working mental model of the relevance levers each one gives you.”
The ad says“Command of the query DSL, mappings, analyzers, and aggregations, and a working mental model of the relevance levers each one gives you.”
The ad says“Deep, hands-on OpenSearch or Elasticsearch expertise, ideally with production search you designed and built yourself.”
The ad says“Proficiency in TypeScript/JavaScript and/or Python for building services, pipelines, and tooling.”
The ad says“A solid grasp of how index and cluster design choices drive performance: sharding, mapping design, query cost, Lucene fundamentals, etc.”
The ad says“A solid grasp of how index and cluster design choices drive performance: sharding, mapping design, query cost, Lucene fundamentals, etc.”
The ad says“Command of the query DSL, mappings, analyzers, and aggregations, and a working mental model of the relevance levers each one gives you.”
The ad says“Deep, hands-on OpenSearch or Elasticsearch expertise, ideally with production search you designed and built yourself.”
The ad says“Proficiency in TypeScript/JavaScript and/or Python for building services, pipelines, and tooling.”
The ad says“A solid grasp of how index and cluster design choices drive performance: sharding, mapping design, query cost, Lucene fundamentals, etc.”
The ad says“Command of the query DSL, mappings, analyzers, and aggregations, and a working mental model of the relevance levers each one gives you.”
The ad says“Proven relevance engineering: you have measurably improved search quality and can explain how you proved it.”
The ad says“A solid grasp of how index and cluster design choices drive performance: sharding, mapping design, query cost, Lucene fundamentals, etc.”
The ad says“Proficiency in TypeScript/JavaScript and/or Python for building services, pipelines, and tooling.”

Values

The ad says“Query understanding: entity extraction, intent classification, spell correction, autocomplete.”
The ad says“Working knowledge of containers and Kubernetes.”
The ad says“Familiarity with OpenSearch Dashboards, Data Prepper, Logstash, or Kafka-based ingestion.”
The ad says“Vector and semantic search experience: embeddings, the OpenSearch k-NN plugin, hybrid ranking, or learning-to-rank.”
The ad says“Query understanding: entity extraction, intent classification, spell correction, autocomplete.”
The ad says“Vector and semantic search experience: embeddings, the OpenSearch k-NN plugin, hybrid ranking, or learning-to-rank.”
The ad says“Query understanding: entity extraction, intent classification, spell correction, autocomplete.”
The ad says“Familiarity with OpenSearch Dashboards, Data Prepper, Logstash, or Kafka-based ingestion.”
The ad says“Working knowledge of containers and Kubernetes.”
The ad says“Vector and semantic search experience: embeddings, the OpenSearch k-NN plugin, hybrid ranking, or learning-to-rank.”
The ad says“Familiarity with OpenSearch Dashboards, Data Prepper, Logstash, or Kafka-based ingestion.”
The ad says“Contributions to OpenSearch, Lucene, or other open source search projects.”
The ad says“Familiarity with OpenSearch Dashboards, Data Prepper, Logstash, or Kafka-based ingestion.”
The ad says“Vector and semantic search experience: embeddings, the OpenSearch k-NN plugin, hybrid ranking, or learning-to-rank.”
The ad says“Experience with SAP or ERP material master and catalog data, including part numbers, cross-references, and unit-of-measure complexity.”
The ad says“Vector and semantic search experience: embeddings, the OpenSearch k-NN plugin, hybrid ranking, or learning-to-rank.”
The ad says“Query understanding: entity extraction, intent classification, spell correction, autocomplete.”
The ad says“Vector and semantic search experience: embeddings, the OpenSearch k-NN plugin, hybrid ranking, or learning-to-rank.”

Search is how our users get their work done. In OMS+, finding the right material, customer, order, or document in seconds can be the difference between a smooth order and a lost one. Often across catalogs with millions of items and decades of enterprise history behind them.

We are looking for a Senior Search Engineer with OpenSearch expertise to join the team. The role is split between product and delivery: you will help design and build the search capabilities that ship with the platform, then work with customer data and customer teams to make them perform in their environments.

The focus throughout is search design and build: index and schema design, indexing pipelines fed from SAP source systems, relevance engineering, and the search APIs our TypeScript product stack consumes. What we need from this role is design judgement. Things like how indexes should be shaped, how queries should be written, and how to prove a relevance change
improved results.

Key Responsibilities

Help design index mappings, analyzers, and sharding strategies suited to large, high-cardinality enterprise catalogs.

Help build and maintain indexing pipelines that keep search
in sync with SAP source systems, including full reindex and incremental update paths.

Design and tune queries: query DSL, scoring and boosting, synonyms, stemming, fuzzy and typo tolerance, faceting, and aggregations.

Contribute to relevance measurement.

Support delivery engagements: profile customer catalogs, tune index and query design, and work with implementation and solution engineering on customer-specific relevance requirements.

Diagnose and fix search performance problems: expensive queries, mapping and analyzer design, sharding choices, and data modeling.

Implement vector and hybrid search alongside lexical search where semantic matching measurably improves results.

Understand and specify platform needs: cluster sizing and configuration, index lifecycle policies, snapshot and upgrade requirements.

Help build clean, well-documented search APIs for the TypeScript product stack, and define the search health and quality signals the platform should surface.

Work with product management on roadmap search priorities, and with solution engineering on multi-tenant considerations.

Document search architecture and relevance decisions so the rest of engineering can reason about them.

Review search-related work from teammates and help raise the team's standards for query design and relevance rigor.

Required Qualifications

Deep, hands-on OpenSearch or Elasticsearch expertise, ideally with production search you designed and built yourself.

Command of the query DSL, mappings, analyzers, and aggregations, and a working mental model of the relevance levers each one gives you.

Proven relevance engineering: you have measurably improved search quality and can explain how you proved it.

A solid grasp of how index and cluster design choices drive performance: sharding, mapping design, query cost, Lucene fundamentals, etc.

Strong debugging ability for search problems: slow or expensive queries, mapping and analyzer mistakes, and results that are wrong in ways users notice before dashboards do.

Experience building high-throughput ingestion and indexing pipelines against systems of record.

Proficiency in TypeScript/JavaScript and/or Python for building services, pipelines, and tooling.

Comfort working directly with customers and delivery teams.

Comfort working alongside a separate platform or IT team: specifying requirements, handing off cleanly, and collaborating on diagnosis rather than reaching for root access.

Clear communication and a habit of documenting decisions, particularly around relevance trade-offs.

Bachelor's degree in Computer Science or a related technical field, or equivalent practical experience.

Preferred Qualifications

Vector and semantic search experience: embeddings, the OpenSearch k-NN plugin, hybrid ranking, or learning-to-rank.

Familiarity with OpenSearch Dashboards, Data Prepper, Logstash, or Kafka-based ingestion.

Experience with SAP or ERP material master and catalog data, including part numbers, cross-references, and unit-of-measure complexity.

Query understanding: entity extraction, intent classification, spell correction, autocomplete.

Multi-tenant search architecture and tenant data isolation.

Experience in a customer-facing implementation, delivery, or professional services capacity.

Working knowledge of containers and Kubernetes.

Contributions to OpenSearch, Lucene, or other open source search projects.

Ready to apply? Please upload your resume in English.

DataXstream Inc., is an equal-opportunity workplace and an affirmative-action employer. We are always committed to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. Discrimination is not welcome on the basis of any other status protected by the laws or regulations in the locations where we work.