Senior Elastic Engineer

Engineering Senior (Elastic)

At Freddie Mac, our mission of Making Home Possible is what motivates us, and it's at the core of everything we do. Since our charter in 1970, we have made home possible for more than 90 million families across the country. Join an organization where your work contributes to a greater purpose.

The Engineering Senior (Elastic) will be an active part of a team of about nine Elastic Engineers providing observability services to the customers across the organization. Elastic is used for several specific cases with varying degrees of criticality. They will bring both technical expertise and coaching to junior members on the team.

The Elastic team works on logging and monitoring for any operational based needs across the organization. Their work directly impacts the health and operational effectiveness of most Freddie Mac systems. They are seen as technical authorities and trusted advisors to a broad range of customers.

You will drive and contribute to the development, optimization, and maintenance of our Elastic stack solutions. As the Engineering Senior (Elastic), you will implement technical solutions and collaborate across departments to ensure our systems are robust, scalable, and innovative.

Design and implement Elastic solutions for data ingestion, storage, application performance management, dashboards, Kibana, anomaly detection, Gen AI and analytics, ensuring high performance and scalability.

Optimize Elastic stack components, including Elasticsearch, Logstash, Kibana, agents, etc.

Monitor and troubleshoot Elastic stack performance, addressing any bottlenecks or issues.

Develop automated solutions for maintaining the Elastic cluster while following best practices for development, deployment, and monitoring.

Stay updated on industry trends and advancements in the enterprise logging and monitoring space.

Bachelor's degree in computer science, Information Technology, or related field (or equivalent experience).

Five to seven years of hands-on experience with Elastic.

Strong expertise in system design, data architecture, automation and scalability.

Strong knowledge of Machine learning concepts and hands-on experience implementing Anomaly Detection ML jobs in Elastic.

Strong proficiency in scripting and programming languages (e.g., Python, NodeJS, Java, etc.).

Hands-on experience with DevOps practices, CI/CD pipelines, and infrastructure automation tools.

Working experience with cloud platforms (AWS, Azure, or GCP) and containerization tools (Docker, Kubernetes).

Familiarity with logging and monitoring tools integrated with Elastic.

Experience using Gen AI tools for code development.

Coordinating the team for maximum efficiency.

Serving as the initial technical contact for internal users and helping other junior team members to grow and succeed.

Ability to build trust with coworkers and customers.

Ability to display transparency is a must.