Senior Data Platform Engineer

Clio is the global leader in legal AI technology, empowering legal professionals and law firms of every size to work smarter, faster, and more securely.  We are transforming the legal experience for all by bettering the lives of legal professionals while increasing access to justice .

We are currently seeking a Senior Data Platform Engineer to join our rapidly growing Security team and help grow our Logging Engineering team. This role is for someone passionate about building scalable logging architectures, designing robust data pipelines, and driving security-focused logging automation at scale. You will help lead, support, and implement the technical architecture for our logging infrastructure while making a tangible impact on our security observability. This role is available to candidates across Canada. If you are local to one of our hubs (Burnaby, Calgary, or Toronto) you will be expected to be in office minimum two days per week for our Anchor Days. What your team does Are you someone who's always probing and asking why, someone who enjoys architecting elegant solutions to complex data pipeline challenges? If so, we have a spot for you on Clio's Logging Engineering team! We are looking for candidates to help design and evolve our logging platform architecture, supporting the team with technical leadership for security logging overall. 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. A day in the life might look like: Contributing to the design and architecture of scalable logging infrastructure including data lakes, security lakes, and multi-tier storage strategies Building and optimizing high-throughput log ingestion pipelines using streaming technologies and cloud-native services Architecting ELK stack deployments, OpenSearch clusters, and hybrid cloud logging solutions Designing data retention, lifecycle management, and cost optimization strategies for petabyte-scale log storage Partnering with the security team lead to develop the logging platform roadmap and technical vision Building Infrastructure as Code for logging infrastructure provisioning, scaling, and disaster recovery Optimizing query performance across multiple data stores and implement efficient data partitioning strategies Designing API and integration layers for log data access, enabling self-service analytics and detection engineering Contributing to capacity planning, performance tuning, and architectural decisions for the logging platform Mentoring engineering team members on logging best practices, data architecture, and platform operations What you may have: 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 Mastery of big data technologies and processing systems including but not limited to Splunk, OpenSearch, ElasticSearch & the ELK Stack, Map Reduce, Hadoop, Cassandra, and other comparable data ingestion & query systems Experience in Data Modelling using one or more methodologies like Kimbal, Inmon, or Data Vault for data-at-rest schema normalization 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