SKILLS WE CAN’T DO WITHOUT:

• Definition and promotion of data architecture products to support project and enterprise analytics strategies.
• Expertise in a minimum of one data modelling tool and modelling convention for instance Erwin, IBM Rational System Architect or Archimate
• An appreciation of end-to-end data architecture solutions including ingestion, storage and relational modelling leveraging industry standard languages including SQL and Python
• Strong leadership skills to lead and mentor a team of data architects and engineers.
• Excellent communication skills, both written and verbal, to effectively communicate with technical and non-technical stakeholders.
• Expertise in facilitating client workshops to gather client requirements and turn these into valuable data models.
• Be able to demonstrate an appreciation of the architecture, design and data related issues that affect the development of the different types of Data Warehouse, Lake, Lakehouse, and Analytics applications.
• Solid grasp across a range of relational and database technology, including Oracle and SQL Server
• Design and Delivery of data governance processes to support management of client data architectures.

SKILLS WE’D LIKE:

• Experience in defining and building enterprise wide and platform conceptual, logical and physical models.
• Proficient in leading small teams of architects/data modellers
• Demonstrate a good understanding of data integration tools, metadata repository tools and their use.
• Appreciation and awareness of the latest concepts around Big Data and its application in Analytics architectures and solutions
• A good understanding of Master Data Management theory and application and the tools that support this.
• Architecture experience in Microsoft Cloud (incl. Azure PaaS and SaaS analytics tools) and exposure to Google Cloud Platform or AWS cloud.
• Experience of migrating/integrating data from on premise environments to Cloud
• Understanding of data security and privacy risks and the ability to implement appropriate measures.
• Knowledge of AI and ML techniques and their application to data-driven insights.
• Knowledge of data science methodologies and tools.
• Knowledge in data visualization tools (e.g., Tableau, Power BI) to create compelling data stories.

YOUR DAY-TO-DAY RESPONSIBILITIES INCLUDE:

We require an experienced Lead Data Architect (over 7 years’ experience in the data arena) to support the delivery of Enterprise and Big Data Architectures with a focus on Analytics and Data Management applications.
As a Lead Data Architect, you will be responsible for leading and the development of numerous data strategies which ensure data-driven decision-making. You will work closely with executive leadership, business units, and IT teams to develop and implement data solutions that align with the clients’ strategic goals. Responsibilities will include designing and overseeing implementation of data models, frameworks, and governance strategies to ensure data is accurate, secure, and accessible to authorised personnel.

THE ROLE INCLUDES.

• Engaging with client enterprise architects and senior managers in developing data architecture strategy to support analytics platform transformation projects.
• Interpreting and delivering impactful strategic plans improving data integration, data quality, and data delivery in support of business initiatives and roadmaps.
• Collaboration with cross-functional teams, including business leaders, data engineers, and data scientists.
• Client facing role working through requirements and delivering data architectures in required formats and using agreed data design/modelling tools.
• Definition of Conceptual, logical and physical data models, Data Flows (including streaming data) and Data Architectures to support Analytics, Data Warehouse and Data Migration requirements.
• Support pre-sales for Analytics and data management solution architectures!
• Design authority and delivery owner of data architecture and models for Analytics & Data Management
• Leading a small team of data modellers
• Map data from operational systems to Analytics systems (on-premises and cloud)