Data Engineer

Boeing Vancouver is seeking a Data Engineer (AI & Analytics), reporting to the Senior Manager of AI & Analytics working out of the Richmond, BC office. 

 This role will help Boeing transform our industry through the application and continuous improvement of advanced analytics and machine learning in the aviation domain. The position will be embedded in a multi-disciplinary data science team producing industry-leading insights, and will use their data management, software development and infrastructure skills to help build bigger, faster, and better cloud-based tools and pipelines. They will be broadly responsible for the design, implementation and support of data pipelines, including the data models, data contracts, and model features. 

Boeing Vancouver Data Engineers may support multiple products, capabilities or teams as needs arise and priorities dictate. This Data Engineer, embedded in the Schedule Reliability & Intelligence will primarily support the development of predictive maintenance services and sensor-based analytics tools in support of such products and services as Boeing Aircraft Health Monitoring, Insight Accelerator and Self-Service Analytics. Areas of practice will including anomaly detection, AI-assisted prognostics and the development of orchestrated agentic AI tools. A successful candidate will exhibit interest and proficiency in the handling of big, time-series data sets, unstructured text data, knowledge graphs, data mesh technology and the implementation of AI/ML Ops pipelines. 

This is a challenging role, requiring versatile problem-solver with keen conceptual mind, ontological thinking, an understanding of data science and valuable data features, as well as computational load and performance. They will work closely with aviation engineers and data scientists in a problem-solving role, helping bridge the gap from data into working data science models. Although primarily responsible for data management, the Data Engineer must be a versatile team player and may be called upon to assist in back-end development, cloud deployment, and even data science from time to time. They must be able to adapt, find the knowledge they need, learn, and make decisions as needs arise. 

Position Responsibilities: 

• Support team data science modeling and problem-solving efforts, primarily in the area of sensor data ingestion, anomaly detection, predictive maintenance tooling. 

• Propose data engineering solutions to support different modeling strategies, including AI/ML and agentic systems. 

• Design, build and support healthy, automated, and repeatable data ingestion and processing pipelines, including delta tables and knowledge graphs, and the implementation of MLOps pipelines. 

• Monitor and maintain data quality, integrity, consistency. Work on systems to monitor system health, data quality and scientific performance. 

• Help design and build scalable, reliable, and high-performance systems and environments. 

• Take part in implementation and support of continuous integration and continuous delivery (CI/CD). 

• Contribute to technical documentation. 

This position is hybrid. This means that the selected candidate will be required to perform some work onsite at Richmond location. This is at the hiring team’s discretion and could potentially change in the future. 

Employer will not sponsor applicants for employment visa status. 

Basic Qualifications (Required Skills/Experience): 

Preferred Qualifications (Education/Experience): 

• Experience working with Databricks, Unity Catalog, Databricks Genie. 

• A technical degree/diploma in a related field of study. 

• Experience working with Large Language Models (LLM) and Natural Language 

• Processing (NLP) technologies, including both discriminative and generative AI models. 

• Experience working with graph databases, knowledge graphs, and their languages (e.g. GraphQL, Cypher). 

• Experience designing and implementing data quality monitoring solutions. 

• Expertise in data modeling principles/methods. 

• Experience with development, deployment and version control tools. 

• Experience with production-level Software Development, including spec-driven development (SDD). 

• Experience in DevOps technologies (e.g. CI/CD, Docker) and practices. 

• Experience with cloud-deployed APIs and micro-services is an asset. 

• Experience in pipeline software is an asset.