Senior Data Platform Engineer, Clio

• This role is for someone passionate about building scalable logging architectures, designing robust data pipelines, and driving security-focused logging automation at scale
• If you have a strong data engineering background with a passion for security and experience building enterprise-scale logging platforms, then we want to talk to you
• Proven Senior-level expertise building enterprise-scale logging and data ingestion systems, typically gained over 5+ years of relevant experience
• Data architecture experience designing data lakes, lake houses, and multi-tier storage architectures for log data
• ELK stack mastery including Elasticsearch cluster design, Logstash pipeline optimization, and Kibana customization
• Streaming data expertise with Apache Kafka, Amazon Kinesis, AWS Glue, Azure Event Hubs, or similar real-time ingestion platforms
• Cloud storage architecture experience with AWS S3, Azure Data Lake, GCS, and intelligent data tiering strategies
• Performance optimization skills for high-volume data ingestion, indexing strategies, and query optimization
• Infrastructure as Code proficiency with Terraform, CloudFormation, or similar for logging infrastructure automation
• Data pipeline engineering experience with Apache Spark, Flink, or cloud-native data processing services
• API and integration design for enabling secure access to log data across multiple consumer applications
• Monitoring and observability expertise for logging platform health, performance metrics, and capacity planning

Desirable

• We are currently seeking a Senior Data Platform Engineer to join our rapidly growing Security team and help establish our new Logging Engineering team
• You will lead the technical architecture for our logging infrastructure while making a tangible impact on our security observability
• We are looking for the right candidate to design and build our logging platform architecture from the ground up, and be a technical leader and advisor for security logging overall
• Design and architect scalable logging infrastructure including data lakes, security lakes, and multi-tier storage strategies
• Build and optimize high-throughput log ingestion pipelines using streaming technologies and cloud-native services
• Architect ELK stack deployments, OpenSearch clusters, and hybrid cloud/on-premises logging solutions
• Design data retention, lifecycle management, and cost optimization strategies for petabyte-scale log storage
• Partner with the security team lead to develop the logging platform roadmap and technical vision
• Build Infrastructure as Code for logging infrastructure provisioning, scaling, and disaster recovery
• Optimize query performance across multiple data stores and implement efficient data partitioning strategies
• Design API and integration layers for log data access, enabling self-service analytics and detection engineering
• Lead capacity planning, performance tuning, and architectural decisions for the logging platform
• Mentor engineering team members on logging best practices, data architecture, and platform operations