Senior Data Architect — Cloud Data Platform & AI

WeDoTech; Greater London; Hybrid; GBP 90,000 - 120,000; Full time

Job summary

WeDoTech in London is seeking a Senior Data Architect to drive a six-month contract on a hybrid basis, with three on-site days. You will shape the evolution of a large cloud-based data ecosystem and work across distributed data platforms to align with ML/AI goals.

The role demands deep engineering credibility, hands-on architecture across technical and senior stakeholder levels, and expertise with AWS, Spark/PySpark, Airflow, and data governance in regulated environments.

Qualifications

• Proven experience in enterprise-scale data architecture for cloud platforms.
• Hands-on architectural delivery across large distributed data ecosystems.
• Experience with AWS cloud data platforms and Spark/PySpark.
• Familiarity with Airflow and ELT/data processing pipelines.
• Knowledge of data lakehouse architectures and data products.
• Exposure to streaming and event-driven data architectures.
• Experience with data governance and design reviews.
• Ability to engage senior stakeholders and engineers alike.
• Experience enabling ML/AI platforms in regulated environments is a plus.

Responsibilities

• Shape the evolution of a large-scale, cloud-based data ecosystem.
• Act as a technically hands-on architect across complex distributed platforms.
• Influence architecture direction across multiple engineering and data teams.
• Operate effectively at technical and senior stakeholder levels.

Skills

Enterprise-scale data architecture

Data governance

Data lakehouse & data products

Streaming & event-driven architecture

Architecture standards & reviews

ML/AI platform enablement

Senior stakeholder communication

Cloud-native data platforms

Regulated environment experience

Tools

Airflow

AWS

PySpark

Spark

Python

AI

Cloud

Job description

WeDoTech in London is seeking a Senior Data Architect to drive a six-month contract on a hybrid basis, with three on-site days. You will shape the evolution of a large cloud-based data ecosystem and work across distributed data platforms to align with ML/AI goals.

The role demands deep engineering credibility, hands-on architecture across technical and senior stakeholder levels, and expertise with AWS, Spark/PySpark, Airflow, and data governance in regulated environments.