This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Data Engineer based in Canada.
This role offers the opportunity to shape modern, enterprise-grade data platforms supporting production AI and business-critical systems. You will define data architecture and platform strategy while owning complex technical decisions across pipelines, warehouses, lakes, governance, and real-time processing. The position combines hands-on engineering with technical leadership, mentorship, and close collaboration across engineering, AI, analytics, product, and leadership teams. You will work on challenging projects where scalability, security, reliability, and performance are essential. An AI-forward approach is central to the role, with modern coding assistants used to accelerate high-quality engineering work. This is an opportunity to influence data engineering standards while delivering solutions with measurable impact for enterprise clients.
Accountabilities:
- Define data architecture and platform strategy across scalable data pipelines, warehouses, data lakes, and distributed data systems.
- Build and optimize data pipelines supporting both batch and real-time processing, with a focus on reliability, scalability, performance, and cost efficiency.
- Establish and enforce data governance, quality, compliance, and engineering standards across data platforms and client engagements.
- Implement monitoring, logging, and alerting for data pipelines and services, while contributing to CI/CD workflows for data deployment and automation.
- Drive data platform modernization and make architectural decisions that account for both immediate requirements and long-term technical tradeoffs.
- Design and implement data contracts and event flows in collaboration with backend, platform, and engineering teams.
- Build production data pipelines supporting AI and machine learning systems, including embeddings, vector stores, RAG data preparation, feature stores, and training and inference data flows.
- Integrate data services with APIs, middleware, and third-party systems to support end-to-end data consumption.
- Partner with leadership on data strategy and translate complex technical considerations into clear decisions and actionable recommendations.
- Collaborate with engineering, analytics, AI, and product teams to align data platforms with broader business and technical objectives.
- Advocate for strong data quality, governance, architecture, and platform practices across teams.
- Establish data engineering standards that improve consistency and quality across the organization.
- Mentor junior and mid-level engineers, supporting their technical development, confidence, and overall impact.
- Use AI-assisted development tools such as Claude, Cursor, and similar technologies to improve engineering productivity and delivery quality.
- Contribute to complex technical challenges with a strong sense of ownership, resourcefulness, and sound judgment.
Requirements:
- 7+ years of professional data engineering experience, including experience leading complex data platform initiatives.
- Strong systems architecture background with deep expertise in distributed data systems.
- Expert proficiency in Python, Scala, and SQL.
- Extensive experience with cloud-native data platforms and enterprise data warehousing.
- Strong expertise in data pipeline orchestration, processing, data transformation, and data modeling.
- Hands-on experience with streaming platforms and real-time processing technologies such as Kafka, Kinesis, or Pub/Sub.
- Strong experience implementing data quality, governance, and compliance frameworks.
- Experience with container orchestration and CI/CD for data systems.
- Demonstrated experience building production data pipelines for AI/ML systems, including embeddings, vector stores, RAG preparation, feature stores, and training/inference workflows.
- Proven technical leadership and mentoring experience across an engineering team or organization.
- Strong stakeholder communication skills, with the ability to translate complex technical concepts for both technical and non-technical audiences.
- Demonstrable day-to-day expertise with AI-forward coding tools such as Claude and Cursor, including an understanding of their practical strengths and limitations.
- Excellent problem-solving skills and the ability to navigate ambiguous technical and business challenges with sound judgment.
- Experience with data mesh or data fabric concepts, lakehouse architectures, or governance framework implementation is a plus.
- Healthcare data experience is a plus.
- AWS certifications, particularly AWS Certified Data Engineer – Associate, are strongly preferred.
- Strong ownership, adaptability, direct communication, attention to detail, and a willingness to work effectively within experienced, multidisciplinary teams.
- Ability and willingness to travel, including approximately 20% U.S.-based travel, as required by the role.
- Must be legally authorized to work in Canada; employment-based visa sponsorship requirements may apply.
- Willingness to travel for a final interview and onboarding if required.
Benefits:
- Remote work: Fully remote position for candidates based in Canada.
- Senior-level scope: Opportunity to shape data architecture, platform strategy, engineering standards, and production AI/ML data systems.
- Technical leadership: High degree of ownership over complex architectural decisions and modernization initiatives.
- Mentorship: Opportunity to mentor junior and mid-level engineers and influence engineering practices across teams.
- AI-forward environment: Daily use of modern AI-assisted development tools and exposure to production AI systems.
- Cross-functional collaboration: Work closely with engineering, AI, analytics, product, and leadership teams.
- Enterprise impact: Build modern data platforms supporting complex, real-world business and AI applications.
- Professional development: Exposure to diverse industries, advanced cloud-native technologies, and challenging enterprise engineering problems.
- Travel: Approximately 20% U.S.-based travel is required, with potential travel for final interviews and onboarding.
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!
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.
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