Job Description
Role: Data Platform Architect - (Lakehouse, DBT, Kubernetes)
Work Mode: Remote
Shift: UK Shift
We are looking for an experienced Data Platform Architect to lead the architecture and evolution of a modern, scalable Data Lakehouse ecosystem. This is a senior-level architecture role for professionals with strong experience in Data Lakehouse architecture, modern data platforms and open-source technologies, with the ability to define technical standards, drive architectural decisions and provide technical direction to engineering teams.
Key Responsibilities:
- Lead the architecture, design and modernization of enterprise-scale Data Lakehouse platforms.
- Define architecture across storage, compute, open-table formats, orchestration and data processing.
- Design decoupled and interoperable data platforms using open-source technologies while minimizing vendor lock-in.
- Establish standards for data pipelines, data modeling, CI/CD, coding practices and technical documentation.
- Design and oversee data governance, security, auditability and data quality frameworks.
- Drive platform performance, scalability, reliability and cost optimization.
- Conduct architecture and code reviews and provide technical guidance to senior engineering teams.
- Maintain Architectural Decision Records and communicate technical roadmaps, trade-offs and architectural decisions to senior stakeholders.
Required Skills & Experience
- 13+ years of experience in Data Engineering, Data Architecture or Data Platform Architecture.
- 8+ years of experience in Data Architecture or Data Platform Architecture.
- 7+ years of experience in DBT and Kubernetes K8
- Strong, hands-on experience designing, migrating or modernizing Data Lakehouse platforms in production environments.
- Strong experience with Databricks and/or Snowflake, particularly from an architecture and platform perspective.
- Strong hands-on experience with dbt Core for data transformation and modeling.
- Strong knowledge of Apache Iceberg and/or Delta Lake, including partitioning, optimization and schema evolution.
- Strong experience with Kubernetes and Docker for containerized data workloads.
- Advanced experience with Apache Airflow and production-grade data orchestration.
- Strong Python and/or PySpark experience.
- Strong understanding of AWS, Azure or GCP cloud ecosystems and storage-compute separation.
- Experience with data governance, DataOps and automated data quality frameworks.
- Ability to translate technical architecture, technical debt and roadmaps into business value for senior leadership.
Great data platforms are not built around a single tool. They are designed to remain scalable, interoperable, secure and cost-efficient as the business evolves. If you enjoy solving complex data architecture challenges and building platforms for the future, this opportunity is worth exploring.