Software Engineer, Data Platform

Accountabilities:

• • Support the design, development, and delivery of scalable data ingestion pipelines and supporting infrastructure.
  • Help migrate legacy data lifecycle management systems to modern platform architectures while minimizing disruption to existing data consumers.
  • Establish and maintain Service Level Indicators (SLIs) and Service Level Objectives (SLOs), supported by dashboards, monitoring, and alerting.
  • Build flexible data storage capabilities that support transactional, analytical, and machine learning workloads.
  • Implement comprehensive monitoring, observability, and incident-response practices across event-driven data pipelines and services.
  • Collaborate with product engineering, analytics, and machine learning teams to define data contracts, functional requirements, technical standards, and platform capabilities.
  • Design and implement a semantic metadata layer that classifies and labels data assets, improving discovery, access policies, and opportunities for cross-product data reuse.
  • Architect and deliver multi-tenant data models that enable secure data sharing and isolation across clients while supporting regulatory and government compliance requirements.
  • Contribute to architectural decisions, technical specifications, system design discussions, and long-term platform strategy.
  • Help connect legacy platforms with modern architectures through robust interfaces, migration strategies, and scalable integration patterns.
  • Apply infrastructure-as-code, cloud-native, containerization, and orchestration practices to build reliable and maintainable data systems.

  Requirements:

  • Demonstrated ability to learn new technologies, frameworks, and technical domains quickly.
  • Product-oriented mindset with an ability to solve complex, big-picture problems while considering end-user needs.
  • Strong understanding of data governance and data lifecycle management, including data quality, lineage, retention, access control, and compliance.
  • Strong knowledge of database technologies and the differences between OLAP and OLTP workloads, with the ability to select appropriate technologies for different use cases.
  • Hands-on experience operating production-scale data warehouse technologies such as Databricks, ClickHouse, Redshift, or comparable platforms.
  • Strong SQL skills, including the ability to write, analyze, and optimize complex queries.
  • Experience designing complex systems and identifying reusable primitives that can support evolving business requirements and future roadmaps.
  • Experience designing and operating data systems on major cloud platforms, particularly AWS or GCP, ideally within multi-cloud environments.
  • Proficiency with containerization and orchestration technologies such as Docker and Kubernetes.
  • Proven experience integrating legacy systems with modern architectures through well-designed interfaces and structured migration strategies.
  • Experience with Infrastructure-as-Code tools such as Terraform, CloudFormation, or similar technologies.
  • Strong communication and collaboration skills, with the ability to facilitate architecture discussions, document technical decisions, write specifications, and work effectively across engineering teams.
  • Familiarity with Elixir for concurrent and fault-tolerant data services is an asset.
  • Experience with data pipeline and streaming technologies such as Airflow, Kafka, Apache Flink, or similar is an asset.
  • Hands-on experience with columnar and OLAP databases such as Databricks or ClickHouse is an asset.
  • Additional GCP experience is an advantage for candidates with a strong AWS background.
  • Candidates who do not meet every listed qualification but demonstrate strong technical potential and relevant experience are encouraged to apply.

  Benefits:

  • Base salary: CAD $130,000–$165,000 annually.
  • Potential additional bonus depending on the position ultimately offered.
  • Comprehensive medical, financial, and other employee benefits.
  • Remote work opportunity based in Toronto, Canada.
  • Opportunity to work on large-scale data infrastructure and modern cloud technologies.
  • Exposure to distributed systems, data governance, machine learning workloads, and multi-tenant architectures.
  • Opportunity to influence the modernization of legacy systems and the evolution of a unified data platform.
  • Collaborative environment working across engineering, analytics, machine learning, infrastructure, and product teams.
  • Inclusive workplace committed to diversity, equal opportunity, and supporting employees from varied backgrounds.
  • Strong emphasis on technical growth, learning, and solving complex engineering challenges.