Senior Software Engineer - Coveo Search

We are seeking a highly skilled and experienced Senior Software Engineer - Coveo Search to help design, build, integrate, and optimize Agilent’s Coveo-powered search experiences across Agilent.com, Community, eCommerce, content discovery, and connected digital experiences. In this role, you will own the technical implementation of Coveo integrations, query pipelines, analytics-driven relevancy improvements, merchandising rules, AI/ML models, and data ingestion pipelines that power customer discovery, self-service, and conversion. You will partner closely with experience, content, commerce, data, analytics, and platform teams to ensure Coveo delivers highly relevant, permission-aware, measurable, and scalable search experiences across products, documents, communities, recommendations, and support journeys.

This role currently follows a hybrid work model. Work arrangements may evolve based on future business needs.

Work with a team of engineers to deliver Coveo search capabilities that are crucial to digital revenue growth, customer self-service, product discovery, and content findability.

Be part of a world class global organization building intelligent search experiences across Agilent.com, Community, eCommerce, content hubs, support journeys, and future AI-enabled experiences.

Design, build, configure, and support Coveo integrations across search interfaces, query pipelines, sources, indexes, APIs, permissions, analytics, and experience-layer components.

Partner with Experience layer teams to integrate Coveo search, filtering, recommendations, and generated-answer capabilities into Agilent.com and Community user experiences.

Implement and tune merchandising rules, boost/bury logic, redirect rules, facet behavior, ranking expressions, synonyms, thesaurus rules, and business-driven search controls.

Support eCommerce search use cases including product discovery, catalog search, product recommendations, category/listing pages, guided search, and search-to-cart journeys.

Support content discovery use-cases including document libraries, technical documentation, application notes, knowledge articles, community content, blogs, wikis, media, and entitled content.

Use Coveo analytics and Snowflake-connected analytics data to identify content gaps, zero-result queries, click-through trends, query-suggestion adoption, generated-answer engagement, and relevancy improvement opportunities.

Configure and improve Coveo models such as Query Suggestions, Automatic Relevance Tuning, Dynamic Navigation, Semantic Encoder, Relevance Generative Answering, and recommendation models where applicable.

Design and support AWS-based ETL and data pipelines that extract, transform, normalize, enrich, and push structured and unstructured data into Coveo indexing pipelines.

Collaborate with product, content, analytics, data engineering, platform, and business teams to continuously tune search results, improve relevancy, and increase measurable customer outcomes.

Bachelor’s or master’s degree in computer science, Engineering, Information Systems, Data Engineering, or equivalent practical experience.

6+ years of relevant software engineering experience with search platforms, digital commerce platforms, content platforms, data integrations, or enterprise web applications.

Hands-on experience with Coveo platform capabilities including sources, indexes, query pipelines, search APIs, usage analytics, permissions, machine learning models, and administration.

Strong understanding of Coveo integrations with front-end experience layers, including headless/API-based search implementations, search boxes, result templates, facets, filters, analytics events, and user context.

Experience with eCommerce search and merchandising concepts including product ranking, boost/bury rules, redirects, synonyms, recommendations, catalog search, product attributes, category pages, and conversion-focused tuning.

Experience supporting content search across structured and unstructured sources such as documents, knowledge articles, application notes, community posts, blogs, wikis, media, product content, and entitled content.

Ability to analyze Coveo usage analytics and Snowflake-connected analytics datasets to identify search quality issues, content gaps, poor performing queries, click behavior, and optimization opportunities.

Experience configuring, evaluating, and improving Coveo AI/ML models such as Query Suggestions, Automatic Relevance Tuning, Dynamic Navigation, Semantic Encoder, Relevance Generative Answering, and recommendation models.

Strong knowledge of data ingestion, indexing, metadata mapping, normalization, enrichment, permissions, incremental updates, delta processing, and content freshness patterns for search platforms.

Experience with AWS-based ETL and data engineering patterns using services such as S3, Glue, Lambda, Step Functions, EventBridge, APIs, databases, and scheduled or event-driven pipelines.

Programming skills in JavaScript/TypeScript, Python, Java, or similar languages used for search integrations, APIs, pipeline processing, automation, and data transformation.

Solid understanding of SDLC, agile delivery, CI/CD, automated validation, observability, production support, and release management for enterprise digital platforms.

Excellent problem-solving skills, analytical mindset, attention to detail, and ability to translate search analytics into technical and business improvements.

Ability to fully utilize agentic development techniques, including AI-assisted coding, automated test generation, code review support, documentation acceleration, and workflow automation, to improve engineering efficiency, delivery speed, and overall development quality.

Deep Coveo implementation experience across both commerce and content search, including query pipeline design, model tuning, merchandising, analytics, and experience-layer integration.

Experience connecting Coveo analytics with Snowflake or enterprise data platforms to build dashboards, monitor KPIs, and drive continuous relevancy improvement.

Experience designing AWS ETL pipelines that support search ingestion, metadata enrichment, permissions, incremental synchronization, bulk updates, and reliable production operations.

Experience integrating search into large-scale B2B eCommerce, customer community, support, documentation, or enterprise self-service experiences.