À propos de ce poste Principal Architect, Data chez Zencore
About this Role
We are looking for a Principal Architect, Data to provide senior technical leadership across our Data practice and client engagements.
This is a highly technical, client-facing role for someone who can move comfortably between enterprise data architecture, hands-on technical execution, and strategic conversations with customers.
At Zencore, Principal Architects are senior technical leaders and the external face of our engineering expertise. They own the end-to-end technical direction and successful technical delivery of complex engagements, while helping define the standards, patterns, and capabilities that allow our engineering organization to scale.
Location: We are looking for candidates in the UK (London Preferred)
Responsibilities
Act as the senior technical referent for complex Data engagements, owning key architecture decisions and ensuring successful technical delivery.
Work directly with clients to understand their business requirements, data landscape, and technical challenges, translating them into scalable solutions on Google Cloud.
Define architectures across data platforms, pipelines, modeling, integration, governance, security, and scalability.
Remain a hands-on technical practitioner, supporting engineering teams with complex implementation challenges and unblocking delivery when needed.
Develop estimates around the skills, resources, and timelines required to deliver Zencore engagements and translate these into clear technical proposals.
Partner closely with Data Engineers, Cloud Architects, and AI/ML teams to design solutions that connect Zencore’s Cloud, Data, and AI capabilities.
Help establish and evolve architectural standards, reusable patterns, and engineering best practices across the Data practice.
Mentor and enable engineers and architects, helping raise the technical bar across the organization.
Support solutioning and pre-sales conversations when deep Data expertise is required, helping customers understand both the technical approach and business value of the proposed solution
Qualifications
Deep experience designing and delivering enterprise-scale data architectures in complex cloud environments.
Strong hands-on experience with the Google Cloud data ecosystem, particularly BigQuery.
Advanced SQL skills and strong experience with Python.
Strong experience with modern data engineering and orchestration technologies such as dbt, Airflow / Cloud Composer, and related tools.
Experience designing scalable data platforms, pipelines, data models, and integration patterns.
Strong understanding of data governance, security, quality, lineage, and observability.
Ability to translate complex business and data requirements into a clear technical vision, architecture, and delivery roadmap.
Ability to move comfortably between high-level architectural thinking and hands-on technical execution.
Strong software engineering fundamentals and understanding of development best practices.
Experience leading large-scale data modernization and transformation initiatives.
Strong stakeholder management skills, with the ability to influence both engineering teams and senior client stakeholders.
Experience in professional services, consulting, or cloud partner environments is highly desirable.
Strong communication skills and the ability to explain complex technical decisions clearly to both technical and non-technical audiences.
Why work at Zencore?
We Are Trusted Experts: Our teams bring deep, hands-on experience to solve the hardest cloud and AI challenges.
We Value Candor: We share unbiased opinions and aren't afraid of hard truths—our clients view us as true subject matter experts.
We Are the Missing Link: We connect our enterprise clients directly to Google Cloud innovation, moving with agility where larger consultancies stall out.
We Remove Uncertainty: We bring structured playbooks that eliminate risk from complex cloud migrations and AI deployments.
We Show Empathy: We build lasting client trust because we understand the real-world operational complexities of enterprise technology.
