Data/AI Engineer

Jobtailor; Vancouver; On-site; CAD 110,000 - 150,000; Full time

Job summary

Jobtailor, Canada (Vancouver area), seeks a senior Data Engineer to design data models, feature stores, and storage patterns for AI workloads. You will build scalable batch and streaming pipelines, implement DataOps and MLOps automation, and ensure governance, reliability, and performance across AI systems.

You will write high-quality, well-tested code, review peers, and optimize systems for scale while collaborating with product and platform teams in a fast-paced environment.

Qualifications

• 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

Responsibilities

• Design and optimize data models, feature stores, and storage patterns for AI workloads
• Build and maintain scalable batch and streaming data pipelines 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 enhance system reliability, scalability, and observability

Skills

Data Modeling

MLOps Automation

SQL Proficiency

Production-Grade Coding

Data Pipeline Development

Education

Bachelor’s degree in computer science or related field

Job description

• 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

• 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

Core Competencies

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 Modeling


• MLOps Automation


• SQL Proficiency


• Production-Grade Coding


• Data Pipeline Development

Hard Skills

• Data Modeling


• SQL


• NoSQL


• DataOps


• MLOps


• Feature Engineering


• Performance Tuning


• Code Review


• Version Control


• Testing

Soft Skills

• Communication


• Collaboration

Industry Keywords

• AI Workloads


• Data Validation


• ML Model Deployment


• Monitoring


• Drift Detection


• Distributed Systems


• Storage Optimization


• Analytics Workloads