Principal Software Engineer – Search Systems

Responsibilities

• Leading architectural design and ensure technical consistency.
• Helping lead our shared search platform – expanding content search, improving relevance via vector and lexical search techniques.
• Building world‑class search systems to enhance users’ search experience.
• Automating processes to assist other teams.
• Collaborating on new ideas to optimize systems and engineering workflows.
• Building relationships with other engineering teams to identify and solve their pain points.
• Working across the stack, from development to infrastructure.
• Designing and developing scalable data processing workflows and microservices using Spark, Spark Streaming, and Airflow.
• Writing clean, modular, and testable code in Python, Java, or Scala, aligned with coding standards and architecture guidelines.
• Lead implementation of system components that span multiple services and modules.
• Diagnose and resolve complex technical issues across distributed systems and data workflows.
• Leading design discussions, code reviews, and architecture sessions to ensure software quality and maintainability.
• Developing and maintaining data models to support analytical and operational use cases.
• Collaborate with cross‑functional stakeholders to translate product requirements into reliable engineering solutions.

• Current expertise with Lucene, Elasticsearch, Solr, or any other search engine, and have industry experience with Semantic Search.
• Proven track record building search systems at scale.
• Proficiency in batch processing technologies, including Spark, Spark Streaming, Airflow.
• Expertise in at least one of Java, Python, Scala.
• Deep understanding of distributed system design, data modeling, and performance tuning.
• Experience with test‑driven development and CI/CD practices.
• Ability to independently drive technical outcomes from problem definition to deployment.
• Familiarity with Agile, Kanban, or other iterative development methodologies.
• Familiarity with vector/embedding‑based search, KNN algorithms.
• Exposure to graph‑based data models or knowledge graph architecture.
• Experience building internal platforms or developer‑facing data tooling.
• Knowledge of observability best practices for data systems (e.g., metrics, logs, alerts).