4 months ago
Vancouver, 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, analytics, operational workflows, machine learning, and AI use cases.
- Build reusable, documented data assets for analytics, product, engineering, and platform teams.
- Design multi-tenant SaaS data structures using dimensional modeling, semantic analytics, and governed access patterns.
- Own asset-based or software-defined orchestration with versioned, observable data assets and dependency contracts.
- 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 translate workflows into reliable data products.
- Contribute to platform architecture, data onboarding standards, reusable engineering patterns, and data platform strategy.
- Support event-oriented and near-real-time data patterns for operational and product use cases.
- Troubleshoot complex data issues, lead root-cause analysis, and improve pipeline and platform resilience.
- Mentor junior and intermediate engineers and review code and technical designs.
Requirements
- At least 5 years of experience in data engineering, software engineering, analytics engineering, or a closely related field.
- Strong production proficiency in Python and SQL and experience with 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 an equivalent.
- Experience designing, building, and maintaining production ETL/ELT pipelines for analytical or operational workloads.
- Strong understanding 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.
- Experience with Unity Catalog or an equivalent metadata and governance layer is preferred.
- Experience with event-driven, streaming, or near-real-time data patterns is preferred.
- Experience building data products for predictive model development, including feature preparation, label definition, and model-training or evaluation pipelines, is preferred.
- Experience supporting generative AI or agent workflows through data preparation, retrieval patterns, or evaluation datasets is preferred.
- Experience in a SaaS product organization with multiple products, domains, tenants, or customer-specific data boundaries is preferred.
- Familiarity with cloud data-platform cost optimization is preferred.
Benefits
- Starting salary is $110,000 CAD, commensurate with experience.
- The role reports to a Technical Lead and operates within a self-management, distributed decision-making organization.
- The company emphasizes collaborative peer-based networks and a diverse and inclusive environment.
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.