
Data Architect (Contract)
- England
- Permanent
- Full-time
- Define and evolve AllSaints' enterprise data architecture to support Project Nova's strategic objectives
- Design and develop conceptual, logical, and physical data models
- Architect the expansion of our data warehouse and BI capabilities, including data lakes and streaming
- Design data pipelines and structures optimized for machine learning models
- Evaluate and recommend data technologies, tools, and platforms
- Participate in the Technical Governance approach, submitting designs for review and participating in architectural standups, working sessions and Design Authority meetings
- Establish and enforce data governance policies, standards, and procedures
- Define data quality rules and implement processes for data validation and cleansing
- Design and implement metadata management strategies
- Architect secure data storage, access controls, and data privacy measures
- Develop and implement Master Data Management (MDM) strategies
- Partner closely with the Enterprise Architect, Integration Architect, business stakeholders, and IT teams
- Collaborate with BI developers and data analysts to optimize data structures
- Provide expert guidance and mentorship to data engineers and developers
- Stay abreast of emerging data technologies and AI/ML trends, proposing innovative solutions
- Extensive Data Architecture Experience in designing and implementing complex enterprise data architectures, data warehouses, and data lakes in a global retail or similar data-intensive industry
- Expert-level proficiency in various data modeling techniques and tools
- Strong hands-on experience with cloud data platforms, specifically Google Cloud Platform - BigQuery
- Experience with leading ETL/ELT tools and data pipeline orchestration (e.g., Dataflow, Apache Airflow, Talend, Informatica)
- Advanced SQL skills and deep knowledge of various database technologies (relational, columnar, NoSQL)
- Practical experience in establishing data governance frameworks, data quality initiatives, and Master Data Management solutions
- Understanding of data requirements for machine learning, including feature stores, data versioning, and MLOps principles
- Familiarity with common retail data domains (e.g., customer, sales, inventory, product, supply chain, marketing data)
- Ability to translate complex technical concepts into clear business terms and effectively communicate with both technical and non-technical stakeholders