Cloud Engineer/ Support Engineer - Cloud

Google Retail Search Engineer

Experience: 3–6 years

Role Type: Retail Search / Integration Engineer

Role Overview

We are looking for a Google Retail Search Engineer to implement and integrate Google Retail Search parallel A/B testing initiative against Elastic Search. The role focuses heavily on product feed ingestion, Google schema mapping, taxonomy/attribute transformation, search API integration, query routing, response transformation, testing and UAT.

Key Responsibilities

• Implement Google Retail Search for the parallel A/B platform.
• Analyze existing product, pricing, inventory, taxonomy and attribute data.
• Map existing product/feed schema to Google Retail Search schema.
• Preserve identifiers, pricing, inventory and business-critical attributes.
• Build feed-generation and transformation processes.
• Extend ingestion pipelines to Google, including batch/full and near-real-time updates where required.
• Integrate Google APIs with Java/microservice applications.
• Contribute to query routing between Google and Elastic.
• Transform Google responses into the existing API/UI format without changing the contract.
• Implement search, filters, facets and autocomplete capabilities.
• Develop data, functional, integration and regression tests.
• Support A/B comparison, discrepancy analysis, UAT and business acceptance.
• Troubleshoot feed, mapping, API and search issues.
• Document mappings, transformations, APIs and test scenarios.
  
  

Required Skills

• 3–6 years software/data/search engineering experience.
• Google Cloud experience; Google Retail Search preferred.
• Strong Java and/or Python.
• REST APIs and JSON.
• Data transformation and ingestion pipelines.
• Strong product/catalog, schema, taxonomy and attribute-mapping knowledge.
• Search API integration and troubleshooting.
• Ability to work across data, application and search layers.
  
  

Good to Have

• Retail/e-commerce experience.
• Large product catalog / SKU-based search experience.
• Cloud, Docker, Kubernetes and CI/CD knowledge.
• Streaming or near-real-time data pipelines.
• Semantic/vector search experience.
• Generative AI, AI Agents / Agentic AI or AI-assisted engineering experience.
• Experience using AI/agents for test automation, data mapping, search evaluation, troubleshooting or engineering productivity.
• BigQuery, Pub/Sub, Dataflow, Cloud Storage or Cloud Run.
• Elastic Search experience.