4 months ago
Toronto, CanadaSenior
Responsibilities
- Design, build, and optimize scalable data pipelines and curated data products using Python, SQL, and distributed compute.
- Develop and maintain raw, refined, and curated data models for reporting, embedded analytics, operational workflows, machine learning, and AI use cases.
- Build reliable, reusable, documented data assets for analytics, product, engineering, and platform teams.
- Design multi-tenant SaaS reporting structures, dimensional models, semantic analytics data, and governed access patterns.
- Own asset-based or software-defined orchestration with versioned data assets, dependency contracts, and observability.
- Improve data processing performance, reliability, observability, and cost efficiency.
- Implement data quality, lineage, governance, and secure access control practices.
- Partner with product, engineering, analytics, and AI stakeholders to develop data products and platform capabilities.
- Contribute to platform architecture, reusable engineering patterns, data onboarding standards, and data platform strategy.
- Support event-oriented and near-real-time data patterns and troubleshoot complex data issues through root-cause analysis.
- Operate within containerized or cloud-native platform infrastructure and understand interactions between data services and platform components.
- Mentor junior and intermediate engineers and review code, designs, and engineering practices.
Requirements
- At least 5 years of experience in data engineering, software engineering, analytics engineering, or a closely related field.
- Strong proficiency in SQL and Python with production experience in distributed compute environments, execution models, partitioning, and optimization.
- Hands-on experience with cloud-native lakehouse platforms and modern data lake storage patterns, including Delta Lake or equivalent.
- Experience designing, building, and maintaining production ETL/ELT pipelines for analytical or operational workloads.
- Strong knowledge of layered data product design, data modeling, dimensional modeling, curated data products, and semantic-ready data structures.
- Familiarity with asset-based or software-defined orchestration, version control, CI/CD practices, and production support for data systems.
- Strong understanding of data quality, observability, governance, lineage, and secure data access patterns.
- Ability to communicate technical trade-offs and collaborate across engineering, product, analytics, and business teams.
- Experience mentoring engineers through code reviews, design reviews, troubleshooting, and shared engineering standards.
- Preferred experience with Unity Catalog or equivalent metadata and governance layers.
- Preferred experience with event-driven, streaming, or near-real-time data patterns.
- Preferred experience building data products for predictive model development, including feature preparation, label definition, and model training and evaluation pipelines.
- Preferred experience supporting generative AI or agent workflows through structured and unstructured data preparation, retrieval patterns, or evaluation datasets.
- Preferred experience in SaaS organizations with multiple products, domains, tenants, or customer-specific data boundaries and with cloud data platform cost optimization.
About iQmetrix
iQmetrix builds SaaS retail management and point-of-sale software for telecom retailers, connecting POS, activations, inventory, drop ship, and omnichannel commerce. Its Interconnected Commerce platform links carriers, retailers, and OEMs to transact, fulfill, and operate across online and in-store channels. Founded in 1999 and headquartered in Vancouver, the privately held company focuses on the wireless/telecom retail market and supports large-scale transaction volumes for North American brands.