About the job
Join Us in Redefining Aviation
At Vertical Aerospace, we are at the forefront of transforming the aviation industry with our innovative electric aircraft, the Valo. This eVTOL (electric, vertical take-off and landing) aircraft is designed to achieve 'zero emissions' and set new benchmarks in safety for air travel.
Our approach diverges from traditional aerospace methods; we are committed to redefining best practices in the industry. As we transition from a prototype-focused enterprise to a rapidly growing SME, the next few years will be pivotal in actualizing our ambitious objectives, including achieving airliner-level safety certification by 2028 for our airline partners.
Your Role as a Data Architect
In this crucial role, you will spearhead the establishment and maintenance of our data architecture across the organization. You will define how data is structured, integrated, and presented across various platforms, ensuring a seamless connection between enterprise and time-series data domains.
You will also possess hands-on involvement, particularly in the development and implementation of data pipelines, models, and platform components. The role requires a balance between long-term architectural strategies and immediate project deliverables, working in close coordination with data engineers, BI developers, and software teams to deliver effective and well-designed solutions.
Key Responsibilities
Define and uphold the target data architecture, encompassing data platforms, models, and integration techniques.
Design scalable data models for enterprise and time-series data, ensuring consistency and reusability.
Establish standards for data modeling, transformation, and pipeline design across various analytical and operational use cases.
Collaborate with data engineering teams to align pipelines with agreed architectural patterns and facilitate downstream analytics.
Contribute actively to the development of data pipelines, models, and platform components as necessary.
Define data exposure through semantic layers and BI tools to ensure consistent metrics and definitions.
Support the integration of data across various systems, including engineering, testing, and enterprise platforms.
Identify and mitigate fragmentation in data sources, pipelines, and reporting outputs.
