Elasticsearch Developer
Job Description
About the Role
We are looking for a Elasticsearch Developer to design, optimize, and maintain data systems built on Index Design & Mappings, Search Relevance Tuning, Aggregations & Analytics. This role combines technical depth in Elasticsearch with the ability to understand business context — you'll work with analysts, engineers, and stakeholders to ensure data is reliable, accessible, and useful. The ideal candidate has hands-on experience with Kibana and Logstash, can diagnose complex query performance issues, and understands both OLTP and OLAP patterns. You'll own data pipeline reliability, query performance, and schema evolution for systems handling millions of records.
Key Responsibilities
- Own Index Design & Mappings implementation and optimization — configuration, customization, and ongoing enhancement based on business needs
- Manage Search Relevance Tuning workflows including setup, user training, and continuous improvement of processes
- Implement and maintain Aggregations & Analytics ensuring seamless integration with existing systems and workflows
- Design and maintain Elasticsearch schemas optimized for both operational and analytical workloads
- Write and optimize complex queries, stored procedures, and data transformation pipelines
- Monitor Elasticsearch performance — query execution plans, resource utilization, and capacity planning
- Build automated ETL/ELT pipelines for data integration from multiple source systems
- Create dashboards and reporting solutions that enable data-driven decision making
- Implement data quality checks, validation rules, and monitoring for data pipeline reliability
- Plan and execute database migrations with zero-downtime cutover strategies
Must-Have Qualifications
- Hands-on experience with Index Design & Mappings — configuration, customization, and troubleshooting in production environments
- Proficiency with Kibana as part of the Elasticsearch development/operations workflow
- 3+ years of hands-on Elasticsearch experience in production environments
- Strong SQL skills — complex queries, window functions, CTEs, and query optimization
- Experience with data modeling — star schemas, normalization, and denormalization trade-offs
- Understanding of ETL/ELT pipeline design and data quality management
- Ability to communicate data insights to both technical and non-technical stakeholders
Nice-to-Have Skills
- Elastic Certified Engineer certification or equivalent validated credential
- Elastic Certified Analyst certification or equivalent validated credential
- Experience with advanced Elasticsearch features: Search Relevance Tuning, Aggregations & Analytics, Logstash Pipelines
- Familiarity with the broader Elasticsearch ecosystem including Logstash and Beats
- Experience with real-time streaming systems (Kafka, Kinesis, or Flink)
- Knowledge of data governance frameworks and data catalog tools
Interview Tips
Technical Coding Exercise
Give a small, realistic Elasticsearch coding challenge that tests fundamentals — clean code, edge case handling, and test writing. Time-box to 45-60 minutes.
Architecture Whiteboard
Present a system design problem relevant to Elasticsearch. Evaluate their approach to scalability, data modeling, and trade-off discussions.
Code Review Simulation
Show a Elasticsearch pull request with both good patterns and subtle issues. Assess what they catch, how they communicate feedback, and what they prioritize.
Past Project Deep-Dive
Have them walk through their most challenging Elasticsearch project. Ask probing questions about architecture decisions, obstacles, and what they learned.
Typical Team Structure
Team Size
2-5 Elasticsearch developers
Reports To
Engineering Manager, Tech Lead, or CTO
Collaborates With
Product Management, QA/Testing, DevOps, Design
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