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.

Summary: 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

Monitoring and observability expertise for logging platform health, performance metrics, and capacity planning

An AI first mentality, where your decision making, coding, and throughput are amplified through your AI usage

Serious bonus points if you have:

Programming proficiency in Python, Ruby, Go, or Scala for custom logging pipeline development and automation

Experience with SQL Data Warehouse Dimensional Modelling using Kimball, Inmon, Business Data Vault or other modelling techniques.

Kubernetes and Container encapsulation for logging architecture within cloud-native application systems.

Security-focused logging experience with SIEM integration, security data lakes, and compliance-driven data retention

Multi-cloud logging