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Experience with search engine platforms like Apache Solr, Elasticsearch, or Algolia. Develop and document business requirements for search engine enhancements, including indexing, ranking, scoring, and user interface changes.
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Resolve issues related to indexing, search, and data ingestion in Elasticsearch. This position includes ownership of data projections at the edge, optimized for multi-tenant retrieval and storage, and operationalizing indexing and ML pipelines.
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Create and manage Elasticsearch Clusters on premise, including configuration parameters, indexing, search and query performance tuning, RBAC security governance and administration.
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We're looking for top-notch search engineering talents in the area of information retrieval, search indexing, Elasticsearch, Lucene, algorithms, relevance & ranking, data mining, machine learning, data analysis & metrics, query processing, multi-lingual search and multi-modal search.
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We sit at the intersection of data and machine learning and build intelligent commerce solutions for AI filled shopper experiences, such as Live Search, Product Recommendations, merchandizing, business intelligence – just to name a few.
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Familiarity with Elasticsearch for data indexing and search. Experience with data visualization libraries such as Deck.gl or Leaflet. Data Engineer Why This is an Exciting Role: As a Data Engineer at Boeing Intelligence & Analytics (BI&A), you will play a pivotal role in developing user-facing applications that leverage cutting-edge technologies to consume and generate information for the customer.
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Proficiency in Elasticsearch, including data indexing, search queries, mappings, and aggregations. The ideal candidate should have a deep understanding of Elasticsearch architecture, data indexing, querying, and visualization techniques.
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Full text indexing and searching technologies such as Lucene and ElasticSearch, tuning indexing and searching for search relevancy and accuracy; using data mining and machine learning tools and algorithms.
$125,000 - $185,000 a yearFull-timeExpandApply NowActive JobUpdated 4 days ago - UpvoteDownvoteShare Job
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May recommend data analytics strategies including enterprise data warehousing, master data management, data quality, data governance, Big Data, business intelligence, visualization.
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Experience with Apache Solr, Elasticsearch, or similar search engine platforms. Develop and maintain search algorithms, including query processing, indexing, ranking, and scoring.
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Data ingestion & enrichment from various sources, webhooks, and REST APIs with JSON/YAML/XML payloads & testing POSTMAN, etc. Hands-on Scripting & Programming in Python, Ansible, bash, data parsing (regex), etc.
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The amount of digital data that we consume and produce on a daily basis has grown exponentially, but the way we access, search, and discover that information hasn't kept up. Experiment with and integrate state-of-the-art techniques in both LLM-augmented indexing and retrieval.
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Application onboarding - capturing requirements, understanding data sources, architecture diagrams, application relationships, etc. Experience with application onboarding - capturing requirements, understanding data sources, architecture diagrams, application relationships, etc.
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Integrate Beats for lightweight data shippers to send data to Elasticsearch. Familiarity with Kibana for data visualization, dashboard creation, and log analysis. Configure and optimize Elasticsearch clusters for performance and reliability.
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Proficiency in designing and implementing complex data models, schemas, and indexing strategies. 8+ years of experience working with NoSQL databases, with a strong focus on MongoDB and ElasticSearch.
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