Senior QA Search Engineer

Job Title: Senior Search QA Engineer

Role Overview

We are seeking an experienced Senior Search QA Engineer to design, champion, and execute the quality assurance strategy for our enterprise search platform. Search at our scale is a complex ecosystem of data pipelines, distributed infrastructure, and real-time relevance tuning. In this role, you won't just look for broken buttons—you will validate index integrity, evaluate complex relevance algorithms, and ensure our search clusters remain highly performant.

As a senior member of the team, you will establish testing frameworks, leverage cloud infrastructure, and own the end-to-end quality metrics. You will be a vital bridge between search engineers, product managers, and business stakeholders, ensuring our agile delivery is both fast and flawless.

Key Responsibilities

• QA Strategy & Architecture: Design, implement, and maintain comprehensive test strategies and automation frameworks specifically optimized for microservices and search architectures.

• Solr Validation & Relevance Testing: Validate complex Apache Solr configurations, including schema updates, custom tokenizers, synonyms, boosting rules, and faceting behaviors. Ensure data consistency between source databases and Solr indexes.

• GCP Infrastructure & Log Analysis: Monitor and analyze system performance and error rates across Google Cloud Platform (GCP) using tools like Cloud Logging, Cloud Monitoring, and BigQuery. Validate that search clusters scale and failover smoothly.

• Agile Leadership & Jira Governance: Architect the bug-tracking workflow within Jira, ensuring defects are prioritized, tracked, and thoroughly documented with rich technical context (e.g., query explain plans, raw JSON payloads, and log traces).

• Knowledge Management: Author master test plans, automated QA runbooks, and search-relevance matrices in Confluence to establish a single source of truth for platform quality.

• Performance & Load Testing: Plan and execute load, stress, and regression testing on search APIs to ensure sub-second latency under peak traffic conditions.

• Mentorship: Provide technical guidance, code reviews, and mentorship to junior QA engineers and analysts.

Required Skills & Qualifications

• Experience: 5+ years of software quality assurance experience, with at least 2–3 years focused on specialized search platforms, data pipelines, or large-scale distributed systems.

• Advanced Solr Expertise: Deep understanding of Apache Solr (or Elasticsearch/Lucene internals). Proven ability to debug complex search queries, analyze score explanations (debugQuery=true), and validate index schemas.

• GCP Proficiency: Hands-on experience testing applications deployed in Google Cloud Platform (or AWS/Azure equivalent), including working with GKE (Google Kubernetes Engine), Pub/Sub, and cloud-native logging/monitoring tools.

• Atlassian Suite Mastery: Advanced proficiency with Jira (including custom filters, dashboards, and agile metrics) and Confluence for maintaining engineering documentation.

• Automation & Scripting: Strong scripting/programming skills (Python, Java, or Bash) to build automated test scripts, parse heavy log files, and validate API endpoints.

• API & Data Testing: Expert-level familiarity with REST API testing tools (Postman, Newman) and strong SQL skills to verify data ingestion pipelines.

Nice-to-Have Attributes

• Experience implementing automated search relevance testing (e.g., using frameworks or scripts to measure NDCG, Precision, or Recall).

• Familiarity with CI/CD tools (GitHub Actions, GitLab CI, or Jenkins) to embed automated search tests into deployment pipelines.

• Understanding of e-commerce behavioral data, clickstream analysis, or basic machine learning concepts related to search (e.g., Learning to Rank).