Elasticsearch and OpenSearch engineer — clusters, relevance, logs

Search is what the user notices first and forgives last, so I treat it as a separate engineering discipline, not a backend attachment. I design and maintain Elasticsearch and OpenSearch clusters end-to-end: lay out your data in clean mappings, select the right field types and build custom analyzers with tokenizers, stemmers and synonym dictionaries for your language and domain. When results behave strangely, I tackle scoring — rewrite queries, tune boosting, function score and ranking — until the top contains exactly what people need, and I back every change with relevance tests, not guesses.

Beyond search itself I cover heavy analytics and observability. I write aggregations for dashboards and reports, design index templates and lifecycle policies so data properly ages, and configure ingest pipelines that parse, enrich and normalize logs and events before they reach the index. On the operations side: I size shards and nodes, plan capacity, tune snapshots and recovery, tighten security and roles, catch slow queries and hot shards quietly eating your latency budget. Whether you're launching a new search feature, migrating between engines or versions, or rescuing a cluster that keeps going red — I work calmly and steadily and leave a system that can be maintained.

I care about the boring things that hold up reliable search: reproducible index builds, re-indexing without downtime via aliases, monitoring you can trust, and documentation your team can follow without me.

— Cluster design, sizing, sharding and capacity planning
— Mappings, custom analyzers, tokenizers and synonyms
— Relevance tuning, scoring, boosting and search quality tests
— Aggregations for analytics, dashboards and reports
— Ingest and log pipelines, index templates and lifecycle policies
— Re-indexing without downtime, snapshots, monitoring and performance analysis