Lutra
Data Engineering Leadership
Toronto, CAN · Staff+
Sponsorship not specifiedDetected 41 days ago
JavaGoScalaFull-Stack DevelopmentSQLCloud PlatformsKubernetesRESTgRPCKafkaMachine LearningSparkAirflowData EngineeringData VisualizationLLMsAI OrchestrationA/B TestingIncident ResponseLeadershipCollaborationMentoring
About the role
- These are high-visibility, high-impact opportunities with a talent-dense, homegrown, bootstrapped, globally competitive Canadian tech success story.
- The company finds itself squarely in the middle of the disruption of search by AI/LLMs, the fragmentation of the media landscape and in a chapter of rapid, transformational growth.
- The work spans petabyte-scale batch and streaming pipelines, low-latency APIs, analytical platforms, and the architecture that connects raw event data to reliable, production-grade data products.
Responsibilities
- This team builds and operates high-scale services exposing aggregated and near real-time data for reporting, analytics, and customer-facing products; shaping API contracts, query abstraction and execution, caching, response design, performance, scalability, and reliability.
- Build systems supporting historical processing, backfills, incremental updates, and near real-time availability without creating unnecessary architectural complexity
- Design for deduplication, late-arriving data, schema evolution, data contracts, reconciliation, and the integrity of business-critical reporting and billing datasets
- Lead the design and implementation of distributed data systems operating under heavy workloads and demanding latency requirements
Requirements
- Depth in comparable technologies and the ability to reason from first principles matter more than matching every tool.
Skills
- They have been quietly bootstrapping and growing in line with revenue for over 2 decades.
- To the present day, the company has not raised money from venture capitalists.
- This is not conventional business-intelligence work.
- This is an intentional and informed cultural practice tailored to the challenge ahead of them.
- Depending on your background, your work may lean toward one or both of the following domains:
Benefits
- Own monitoring, alerting, incident response, root-cause analysis, workflow health, and the mechanisms that make system behaviour visible
- Improve observability, service health, incident response, workflow reliability, technical debt management, and post-incident learning
- Build canonical datasets and processing workflows supporting reporting, billing, products, analytics, experimentation, and machine-learning use cases
- Work across Data Engineering, Data Systems, Application Engineering, Product, analytics, machine learning, and experimentation teams to turn platform capabilities into business outcomes
This listing is sourced directly from Lutra's careers page and normalized into a canonical job model.