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.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether? 

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

#LI-CL1