Lead Data Architect, NTT

• Education: Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field
• 10+ years of experience in data architecture and modelling
• Proven experience in data modelling, data architecture, and data products design
• Proven experience and expertise in data modelling standards, techniques (e.g. dimensional model, 3NF, Vault 2.0)
• Familiarity with both analytical and real-time/ streaming data solutions
• Hands-on experience with data modelling tools (e.g., Erwin, Lucidchart, SAP PowerDesigner)
• Knowledge of Python and SQL (e.g., Snowflake or a similar warehousing technology, real-time systems)
• Experience in AWS cloud services, particularly Lambda, SNS, S3, and EKS, API Gateway
• Knowledge of data warehouse design, ETL/ ELT processes, and big data technologies (e.g., Snowflake, Spark)
• Familiarity with data governance and compliance frameworks (e.g., GDPR, HIPAA)
• Strong communication and stakeholder management skills
• Analytical mindset with attention to detail
• Ability to lead and mentor teams on best practices in data modelling

Desirable

• We are seeking a highly skilled and experienced Data Architect to join our dynamic team
• The ideal candidate will have a strong background in full data modelling life cycle, e.g. design, implement, and maintain complex data models that align with organisational goals and industry standards
• This role requires a deep understanding of data architecture, data modelling methodologies, and ideally in real-time data integrations
• The successful candidate will collaborate with cross-functional teams to ensure optimal data structures that support business intelligence, analytics, and operational requirements
• This role is creating, assuring, and overseeing the implementation of data models within analytical and real-time streaming domains
• Develop conceptual, logical, and physical data models to support data analytics, streaming and data products implementation
• Define and maintain data architecture standards, principles, and best practices
• Ensure data models are aligned with business requirements and scalable for future needs
• Work closely with business stakeholders, data engineers, data solution architects, and data product teams to gather requirements and design solutions
• Provide guidance on data integration, transformation, and migration strategies
• Establish and maintain enterprise data models, data dictionaries, metadata repositories, and data lineage documentation
• Ensure data models comply with organisational policies and regulatory requirements
• Optimise data products and their components for performance, scalability, and reliability
• Evaluate and recommend data modelling tools and technologies
• Stay updated on industry trends and emerging technologies in data architecture
• Identify and resolve data inconsistencies, redundancies, and performance issues
• Provide technical leadership in addressing complex data-related challenges