Senior Data & AI Platform Data Engineer

Boeing•Seattle, WA

About The Position

At Boeing Digital Services, you’ll be a part of a team that creates innovative digital solutions and analytics that drive the future and evolution of Digital Services and enable our customers to transform the way they do business. Using agile digital technology, our solutions optimize all aspects of the aircraft operations and maintenance ecosystem including safety, environmental sustainability, and efficiency. Boeing Vancouver is seeking a Data Engineer, reporting to the Data & AI Platform Manager, working out of the Seattle, WA office. The Data & AI Platform Data Engineer (Level 4) 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 & AI Platform 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. 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.

Requirements

Minimum 3-year Cloud deployment experience (Azure preferred)
Minimum 3 years’ experience in relational and non-relational database technologies
Minimum 3-years’ experience supporting data science and analytics projects and/or infrastructure
Minimum 3-years building production grade ETL pipelines and Data Mesh and/or Data Bricks
Must be proficient in Python, SQL, PySpark
Experience working with Large Language Models (LLM) and Natural Language

Nice To Haves

A technical degree/diploma in a related field of study
Processing (NLP) technologies
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
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

Responsibilities

Support data eco-system via data Ingestion and Modelling, Operational Monitoring, Onboarding, Data Health, governance
Propose data engineering solutions to support different modelling strategies
Design, build and support healthy, automated, and repeatable data ingestion and processing pipelines
Raw data ingestion, cleansing, and data contracts
Design data models and data contracts
Monitor and maintain data quality, integrity, consistency
Help design and build scalable, reliable, and high-performance systems and environments
Effectively contribute to building the overall knowledge and expertise of the technical team
Participate in work and code reviews with the team
Take part in implementation and support of continuous integration and continuous delivery (CI/CD)
Work on systems to monitor system health, data quality and scientific performance
Implement data access-control for compliance with data governance policies
Contribute to technical documentation
Collaborate with developers, data analysts, data scientists and organizational leaders to identify opportunities for process improvements
Exhibit sound judgment, keen eye for details and tenacity for solving difficult problems.
Experience working and supporting a data mesh on Databricks