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RBC

Data/AI Engineer

RBC

Data/AI Engineer transforming raw, unstructured data into clean datasets for AI-driven innovation at RBC. Collaborating with ML Developers and Researchers to design and implement data pipelines.

Posted 7/22/2026full-timeVancouver • 🇨🇦 CanadaMid-LevelSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and optimizing data models and pipelines for AI workloads, with a strong foundation in DataOps and MLOps practices. Proficient in production-grade coding and collaboration across cross-functional teams to deliver high-quality technical solutions.

Highest-signal resume keywords
Data ModelingMLOps AutomationSQL ProficiencyProduction-Grade CodingData Pipeline Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Data ModelingSQLNoSQLDataOpsMLOpsFeature EngineeringPerformance TuningCode ReviewVersion ControlTesting
Soft Skills
CommunicationCollaboration
Industry Keywords
AI WorkloadsData ValidationML Model DeploymentMonitoringDrift DetectionDistributed SystemsStorage OptimizationAnalytics Workloads

Tech Stack

Tools & technologies
Distributed SystemsNoSQLSQL

About the role

Key responsibilities & impact
  • Design and optimize data models, feature stores, and storage patterns to ensure performance, reliability, and governance across AI workloads
  • Build and maintain scalable batch and streaming data pipelines that ingest, transform, and curate high-quality datasets for AI and agentic workflows
  • Implement DataOps and MLOps automation, including CI/CD pipelines, data validation, ML model deployment, monitoring, and drift detection
  • Write production-quality code, conduct thorough code reviews, troubleshoot performance issues, and continuously enhance system reliability, scalability, and observability

Requirements

What you’ll need
  • Bachelor’s degree in computer science, Software Engineering, or related field, plus experience in data, software, ML, or AI engineering
  • Strong SQL and database fundamentals (both relational and NoSQL)
  • Proficiency in production-grade coding with strong software engineering fundamentals including version control, testing, and code review practices
  • Deep expertise in data modeling, distributed systems, storage optimization, and performance tuning for large-scale AI and analytics workloads
  • Solid understanding of the ML data lifecycle, including feature engineering, model integration, deployment support, and monitoring
  • Excellent communication and collaboration skills with the ability to translate complex business requirements into well-architected technical solutions; proven ability to work effectively across product, platform, and cross-functional engineering teams

Benefits

Comp & perks
  • A comprehensive Total Rewards Program including bonuses and flexible benefits
  • Competitive compensation
  • Commissions
  • Stock options where applicable
  • Leaders who support your development through coaching and managing opportunities
  • Ability to make a difference and lasting impact from a local-to-global scale