Sr Elastic Engineer - REMOTE

Elastic Engineer.

Location: Remote

Duration: 12 Month

Required Qualifications:

1. Elastic Certification:
   

• Certified Elastic Engineer or higher (e.g. Certified Elastic Analyst Certified Elastic Developer).
  

1. Experience:
   

• Seniorlevel experience (typically 5 years) working with Elastic Stack (Elasticsearch Logstash Kibana Beats).
  
• Proven track record of designing implementing and managing Elastic Stack solutions in production environments.
  

1. Clearance:
   

• Ability to obtain a public trust clearance.
  

Technical Skills:

1. Elastic Stack Expertise:
   

• Deep understanding of Elasticsearch architecture including indexing search and query performance optimization.
  
• Experience with Logstash for data ingestion and transformation.
  
• Proficiency with Kibana for data visualization and dashboard creation.
  
• Knowledge of Beats for lightweight data shipping.
  

1. Data Management:
   

• Experience with data modeling schema design and data lifecycle management in Elasticsearch.
  
• Proficiency in managing largescale data sets and ensuring high availability and scalability.
  

1. Scripting and Automation:
   

• Proficiency in scripting languages such as Python Bash or PowerShell for automation tasks.
  
• Experience with configuration management tools like Ansible Puppet or Chef.
  

1. Security:
   

• Knowledge of securing Elastic Stack deployments including user authentication rolebased access control and encryption.
  
• Familiarity with compliance standards and best practices for securing sensitive data.
  

1. Performance Tuning:
   

• Skills in optimizing Elasticsearch performance including shard allocation indexing strategies and query optimization.
  

1. Monitoring and Troubleshooting:
   

• Experience with monitoring tools and techniques for Elastic Stack.
  
• Strong troubleshooting skills to diagnose and resolve issues in Elastic Stack deployments.
  

Soft Skills:

1. Communication:
   

• Excellent verbal and written communication skills to interact with stakeholders and team members.
  
• Ability to explain complex technical concepts to nontechnical audiences.
  

1. ProblemSolving:
   

• Strong analytical and problemsolving skills to address challenges and optimize solutions.
  

1. Collaboration:
   

• Ability to work effectively in a team environment and collaborate with crossfunctional teams.
  

Additional Preferred Skills:

1. Cloud Platforms:
   

• Experience with deploying and managing Elastic Stack on cloud platforms such as AWS Azure or Google Cloud.
  

1. DevOps Practices:
   

• Familiarity with DevOps practices and tools including CI/CD pipelines.
  

1. Big Data Technologies:
   

• Knowledge of other big data technologies and frameworks such as Hadoop Spark or Kafka.
  

1. Machine Learning:
   

• Experience with Elastics machine learning features for anomaly detection and predictive analytics.
  

Required Qualifications: Elastic Certification: Certified Elastic Engineer or higher (e.g., Certified Elastic Analyst, Certified Elastic Developer). Experience: Senior-level experience (typically 5+ years) working with Elastic Stack (Elasticsearch, Logstash, Kibana, Beats). Proven track record of designing, implementing, and managing Elastic Stack solutions in production environments. Clearance: Ability to obtain a public trust clearance. Technical Skills: Elastic Stack Expertise: Deep understanding of Elasticsearch architecture, including indexing, search, and query performance optimization. Experience with Logstash for data ingestion and transformation. Proficiency with Kibana for data visualization and dashboard creation. Knowledge of Beats for lightweight data shipping. Data Management: Experience with data modeling, schema design, and data lifecycle management in Elasticsearch. Proficiency in managing large-scale data sets and ensuring high availability and scalability. Scripting and Automation: Proficiency in scripting languages such as Python, Bash, or PowerShell for automation tasks. Experience with configuration management tools like Ansible, Puppet, or Chef. Security: Knowledge of securing Elastic Stack deployments, including user authentication, role-based access control, and encryption. Familiarity with compliance standards and best practices for securing sensitive data. Performance Tuning: Skills in optimizing Elasticsearch performance, including shard allocation, indexing strategies, and query optimization. Monitoring and Troubleshooting: Experience with monitoring tools and techniques for Elastic Stack. Strong troubleshooting skills to diagnose and resolve issues in Elastic Stack deployments. Soft Skills: Communication: Excellent verbal and written communication skills to interact with stakeholders and team members. Ability to explain complex technical concepts to non-technical audiences. Problem-Solving: Strong analytical and problem-solving skills to address challenges and optimize solutions. Collaboration: Ability to work effectively in a team environment and collaborate with cross-functional teams. Additional Preferred Skills: Cloud Platforms: Experience with deploying and managing Elastic Stack on cloud platforms such as AWS, Azure, or Google Cloud. DevOps Practices: Familiarity with DevOps practices and tools, including CI/CD pipelines. Big Data Technologies: Knowledge of other big data technologies and frameworks, such as Hadoop, Spark, or Kafka. Machine Learning: Experience with Elastic's machine learning features for anomaly detection and predictive analytics.