Elasticsearch / OpenSearch

Uses Elasticsearch / OpenSearch at a basic level. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.

Understands full-text search concept. Performs basic queries via elasticsearch-py. Knows the difference between match and term queries. Indexes documents.

Uses Elasticsearch / OpenSearch at a basic level in Power BI/Tableau. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.

Uses Elasticsearch / OpenSearch at a basic level in pandas/Jupyter. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.

Uses Elasticsearch/OpenSearch at a basic level in Airflow/dbt. Performs simple tasks using established templates. Understands basic concepts and follows team practices.

Uses Elasticsearch/OpenSearch at basic level in transformers/vLLM. Performs simple tasks using ready templates. Understands basic concepts and follows team practices.

Knows Elasticsearch basics: indexes, documents, basic queries. Performs simple full-text search queries for NLP tasks. Understands analyzer and tokenizer concepts.

Independently implements Elasticsearch / OpenSearch tasks. Understands internals and optimizes performance. Writes tests.

Designs mappings and analyzers. Uses bool queries, aggregations, highlights. Configures indexing via index templates. Optimizes relevance through boosting and function_score. Monitors cluster health.

Independently implements tasks with Elasticsearch / OpenSearch in Power BI/Tableau. Understands internals and optimizes performance. Writes tests using data validation.

Independently implements tasks with Elasticsearch / OpenSearch in pandas/Jupyter. Understands internals and optimizes performance. Writes tests using data validation.

Independently implements tasks with Elasticsearch/OpenSearch in Airflow/dbt. Understands internals and optimizes performance. Writes tests with great_expectations.

Independently implements Elasticsearch/OpenSearch tasks in transformers/vLLM. Understands internals and optimizes performance. Writes tests using eval harness.

Independently configures Elasticsearch for NLP tasks: custom analyzers for multilingual text, mapping for NER annotations, aggregations for text analytics. Optimizes relevance via BM25 tuning.

Designs search architecture: custom analyzers for Russian/English, synonym filters, fuzzy matching. Optimizes indexes: sharding strategy, reindex without downtime. Implements aggregations for analytics.

Designs search architecture for a service. Optimizes index lifecycle management. Configures cross-cluster search. Implements autocomplete and suggest. Designs denormalized mappings for performance.

Designs Elasticsearch/OpenSearch solutions for BI analytics: Kibana dashboard architecture, aggregation pipeline optimization, and real-time data visualization. Optimizes index patterns for time-series reporting and metric exploration.

Designs Elasticsearch/OpenSearch solutions for analytical workloads: complex aggregation queries, cross-cluster search for distributed datasets, and custom analyzers for text mining. Optimizes for analytical query patterns and large-scale data exploration.

Designs search index for data catalog: mapping, custom analyzers for metadata, nested documents for lineage. Optimizes bulk indexing pipeline for millions of catalog records.

Designs Elasticsearch/OpenSearch solutions for production systems. Optimizes performance and scalability. Chooses between alternative approaches. Mentors the team.

Designs Elasticsearch clusters for production NLP systems. Integrates dense vector search for semantic retrieval, optimizes performance for large text corpora with millions of documents.

Defines full-text search strategy for the product: engine selection, indexing schema, synchronization pipeline with the main database. Standardizes search-as-a-service approach.

Designs search platform for the company. Configures multi-tenant search. Optimizes cluster performance. Implements search observability.

Defines Elasticsearch/OpenSearch architecture for BI organization: index lifecycle management, Kibana space governance, and dashboard performance standards. Establishes search query optimization guidelines and data retention policies.

Defines Elasticsearch/OpenSearch standards for analytical teams: index design conventions, query performance benchmarks, and data ingestion pipeline governance. Drives adoption of search-powered analytics and log-based data exploration.

Defines search strategy for data platform: Elasticsearch for metadata discovery, full-text search across dataset descriptions. Implements ILM for index lifecycle management.

Defines Elasticsearch/OpenSearch architectural decisions at product level. Establishes standards. Conducts design reviews and defines technical roadmap.

Defines Elasticsearch usage standards for the NLP team. Establishes best practices for text indexing, performance monitoring, and scaling search clusters.

Designs search platform at organizational level. Defines scaling and fault tolerance strategy. Evaluates alternatives: Meilisearch, Typesense, OpenSearch for different use cases.

Defines company search strategy. Evaluates Elasticsearch vs OpenSearch vs Meilisearch. Designs search at scale.

Defines enterprise Elasticsearch/OpenSearch strategy for BI and observability. Evaluates search platform evolution and migration paths. Shapes organizational standards for search-powered analytics and real-time data exploration.

Defines enterprise search analytics strategy spanning Elasticsearch/OpenSearch and complementary tools. Evaluates search technology evolution and shapes organizational data exploration architecture. Drives adoption of semantic search and vector-powered analytics.

Designs data mesh search infrastructure: federated search across domain catalogs, ML ranking for data discovery, cross-cluster search.

Defines Elasticsearch/OpenSearch strategy at organizational level. Evaluates new technologies and approaches. Establishes enterprise standards and reference architectures.

Shapes enterprise search infrastructure strategy at organizational level. Defines architectural patterns for scaling text search and semantic retrieval for the NLP platform.