AI & Analytics Data Engineer — Cloud Pipelines & ML Ops

The Boeing Company; Richmond; Hybrid; CAD 69,000 - 123,000; Full time

Job summary

Boeing Vancouver is seeking a Data Engineer (AI & Analytics) to join the AI & Analytics team in Richmond, BC. You will design, build, and support data pipelines, models, and features for cloud-based tools and predictive maintenance services across Boeing products.

The role emphasizes big time-series data, knowledge graphs, and AI/ML Ops, collaborating with aviation engineers and data scientists to bridge data and working models. Hybrid work with Richmond onsite.

Qualifications

• Minimum 2 years cloud deployment experience (Azure preferred).
• 2+ years in relational and non-relational databases.
• 2+ years supporting data science, AI/ML and analytics projects.
• 1+ year of Python experience.
• Legally able to work in Canada.
• Eligible to handle US export-controlled data.

Responsibilities

• Support data science modeling and problem-solving in sensor data ingestion and predictive maintenance tooling.
• Propose data engineering solutions for AI/ML and agentic systems.
• Design and maintain automated, scalable data pipelines including delta tables and knowledge graphs; implement MLOps pipelines.
• Monitor data quality and system health; ensure performance of data systems.
• Participate in CI/CD implementation and provide technical documentation.

Skills

Python

Azure

Cloud deployment

Data pipelines

Big data

AI/ML

Knowledge graphs

Education

Technical degree/diploma

Tools

Databricks

Unity Catalog

Databricks Genie

GraphQL

Cypher

Docker

CI/CD

Job description

Boeing Vancouver is seeking a Data Engineer (AI & Analytics) to join the AI & Analytics team in Richmond, BC. You will design, build, and support data pipelines, models, and features for cloud-based tools and predictive maintenance services across Boeing products.

The role emphasizes big time-series data, knowledge graphs, and AI/ML Ops, collaborating with aviation engineers and data scientists to bridge data and working models. Hybrid work with Richmond onsite.