Data Engineer – Data Platform (AI-Enabled)

New

JobgetherData Platform, AI

CanadaFull-Time

Salary not disclosed

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Job Details

Strong professional experience in data engineering, including SQL, data modeling, production data processing pipelines, testing, documentation, and data integrations.
Demonstrated ability to build high-quality data products with clear ownership, definitions, testing practices, lineage, governance, and operational reliability.
Strong understanding of AI-ready semantic modeling, business metrics, secure API-based data exposure, and self-service analytics.
Platform or product-oriented mindset with experience building reusable capabilities.
Solid understanding of cloud infrastructure, DevOps practices, data architecture, and production engineering environments.
Demonstrable experience using AI tools such as Cursor, Claude, ChatGPT, GitHub Copilot, or similar in real engineering delivery.
Ability to explain the impact of AI on development workflows, including validation of AI-generated outputs.
Practical experience using AI for coding, testing, debugging, documentation, technical analysis, and system design.
Experience coaching teammates and influencing the adoption of AI-first development practices.
Experience building machine-learning pipelines for enterprise-class software.
Strong communication, ownership, initiative, and collaboration skills.

Build and maintain trusted, well-modeled, production-grade data products supporting customers, product and engineering teams, and internal business users.
Partner with product, engineering, customer-facing teams, and business stakeholders to translate data needs into scalable platform capabilities.
Design and implement a semantic layer providing consistent business definitions, metrics, and data models for humans, BI tools, and AI systems.
Support a federated BI approach to enable platform consumers to safely and independently develop insights.
Utilize AI tools for daily engineering tasks including development, testing, documentation, debugging, and system design.
Establish AI-first development patterns to increase productivity while maintaining security and governance.
Develop reusable data engineering capabilities across pipelines, integrations, testing, and lineage.
Contribute to cloud infrastructure, DevOps, and platform engineering initiatives.
Build and support machine-learning data pipelines for enterprise-grade use cases.
Support ongoing operation and reliability of production services, including participation in on-call and incident response.

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