Principal Data Engineer

As a Principal Data Engineer at Saviynt, you will lead the development and optimization of ElasticSearch, ensuring smooth upgrades and transitions. You'll implement performance tuning, design multi-tenant solutions, and maintain high availability through monitoring and alerting. Collaborating cross-functionally, you’ll integrate ElasticSearch with various applications while providing mentorship to your peers in best practices and optimization techniques.

To qualify for the Principal Data Engineer position at Saviynt, you should have over 8 years of experience in Data Engineering or a related field, focusing on ElasticSearch administration. Proven experience in upgrading ElasticSearch versions in a production environment and expertise in performance tuning and security best practices are crucial. Familiarity with cloud platforms, containerized environments, and infrastructure automation tools will greatly enhance your application.

The Principal Data Engineer plays a pivotal role in modernizing Saviynt's data infrastructure by leading ElasticSearch upgrades, ensuring data indexing efficiency, and optimizing query performance for large-scale datasets. This role is vital for maintaining system resilience and security while enhancing the overall user experience with faster and more relevant search capabilities.

Success as a Principal Data Engineer at Saviynt requires strong problem-solving and analytical skills, a deep understanding of ElasticSearch performance tuning, and experience with multi-tenant architecture. Proficiency in cloud platforms like AWS and Azure, as well as experience with scripting and automation tools, are essential to effectively manage complex distributed systems and drive innovation in our data practices.

The work culture for a Principal Data Engineer at Saviynt is collaborative and inclusive. We prioritize innovation, knowledge sharing, and personal development. The team is composed of skilled professionals who are committed to excellence while supporting each other in a dynamic environment. At Saviynt, your ideas and insights are valued, making it an exciting place to grow your career.

When upgrading ElasticSearch from version 6.x to 7.x, I would assess current cluster configurations and validate compatibility. Then, I'd carefully plan the upgrade with minimal downtime, possibly using blue-green deployment strategies, while ensuring robust data indexing and availability during the transition.

To optimize ElasticSearch cluster performance, I focus on best practices such as efficient data modeling, tuning query performance through appropriate indexing strategies, adjusting the number of shards, and employing caching. Monitoring metrics and user feedback also help fine-tune overall performance.

Ensuring data security in ElasticSearch involves implementing TLS encryption, role-based access controls, and regularly auditing security settings. I advocate for secure data storage practices and compliance with industry standards to protect sensitive information.

In my previous roles, I've implemented multi-tenant architecture in ElasticSearch by configuring index aliases and applying filtering based on tenant identifiers. This ensures data isolation between tenants while maximizing resource utilization and simplifying management.

When troubleshooting performance issues in ElasticSearch, I analyze query logs and cluster health metrics to identify bottlenecks. I also inspect index settings and examine node performance to implement targeted optimizations and resolve inconsistencies.

I have integrated ElasticSearch with Java applications, specifically using the Spring Boot framework to create RESTful APIs. This integration allows for seamless search capabilities and enhances user interactions by providing real-time search and analytics functionality.

Automation is critical for managing ElasticSearch effectively. I utilize infrastructure-as-code tools like Terraform and Ansible to streamline deployment and configuration processes. This not only reduces manual errors but also ensures consistent environments across different stages of development.

In a previous role, I led a project that involved re-architecting our ElasticSearch cluster for optimal query performance. By revising our data sharding strategy and introducing more efficient indexing methods, I was able to achieve significant reductions in query response times and overall system load.

I adopt a proactive monitoring approach using tools like Kibana and custom alerting systems to keep track of key performance metrics, such as query latency and resource utilization. This helps identify potential issues early and allows for data-driven decisions to maintain high availability.

Balancing performance and resources involves a careful analysis of data workloads and user queries. I establish efficient indexing strategies, optimize resource allocation for nodes, and utilize caching mechanisms to ensure that we deliver fast, reliable performance without unnecessary resource consumption.