Data Architect

Key Responsibilities

- Design and maintain enterprise data and analytics architectures supporting the organization’s analytical data platform.
- Develop data models and solution architectures that translate business and technology requirements into scalable technical solutions.
- Define and promote dimensional modelling standards and practices across enterprise data initiatives.
- Design solutions based on Medallion Architecture principles, including the organization and movement of data across the different platform layers.
- Design the structure of data systems, including databases, data warehouses, data lakes, lakehouses, and enterprise data products.
- Translate requirements from the Chief Data Office, product owners, and technology teams into technical architecture and data models.
- Work closely with data engineering teams to design ingestion patterns and ensure data is correctly integrated into the Medallion Architecture.
- Establish and manage data-product design principles, ensuring data assets are trusted, reusable, scalable, and aligned with organizational objectives.
- Define data schemas, metadata requirements, storage approaches, access controls, and data-quality standards.
- Guide architectural decisions relating to data integration, data transformation, security, performance, and scalability.
- Define long-term technology strategies and roadmaps across data engineering, analytics, and data platforms.
- Lead or participate in architecture reviews, technical governance forums, and quality-assurance activities.
- Simplify existing data and analytics architecture by identifying reusable patterns, shared services, and opportunities for optimization.
- Collaborate with data engineers, data-quality specialists, product owners, analysts, and other IT stakeholders.
- Evaluate and recommend emerging technologies for data management, storage, analytics, artificial intelligence, and machine learning.
- Create and maintain architectural documentation, standards, principles, and solution designs.

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Requirements

- Bachelor’s degree in Computer Science, Data Science, Engineering, Information Systems, or a related discipline.
- Approximately two to five years of relevant experience in data architecture, data modelling, solution architecture, or related data disciplines.
- Demonstrated experience designing data models for enterprise data warehouse, data lake, lakehouse, or analytical data platform initiatives.
- Strong knowledge of dimensional modelling concepts and techniques.
- Strong understanding of Medallion Architecture and its application within modern analytical data platforms.
- Experience designing technical solutions for data integration, transformation, storage, and enterprise analytics.
- Experience working with a major data platform, such as Databricks, Snowflake, Amazon Redshift, or a comparable technology.
- Experience translating business and technology requirements into logical and physical data models and technical architecture.
- Understanding of entity-relationship modelling, metadata management, data quality, and data governance principles.
- Experience designing ingestion patterns and collaborating with data engineering teams to implement data solutions.
- Knowledge of ETL or ELT processes, data warehousing, data lakes, lakehouses, and business intelligence technologies.
- Strong SQL knowledge and the ability to understand data transformations and technical implementation requirements.
- Ability to assess architectural options and make recommendations based on business needs, technical requirements, and strategic objectives.
- Strong analytical, problem-solving, and strategic-thinking skills.
- Ability to communicate complex architectural concepts clearly to stakeholders with different levels of technical knowledge.
- Ability to collaborate effectively across complex, cross-functional, and globally distributed teams.
- Strong written and verbal communication skills in English.
- Flexibility to attend occasional meetings outside standard local working hours to collaborate with global colleagues.

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Preferred Qualifications and Experience

- Hands-on experience with Microsoft Fabric.
- Experience designing solutions within Microsoft Azure.
- T-SQL experience.
- Familiarity with data governance practices and frameworks.
- Experience with Databricks, PySpark, Power BI, or Tableau.
- Experience working with real-time data integration or distribution technologies.
- Knowledge of artificial intelligence and machine learning architectures and workloads.
- Experience developing reusable enterprise data products and shared architectural patterns.

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