Head of Data Architecture

KEY ACCOUNTABILITIES:

• Data Architecture Design: Lead data architecture design to support real-time event streaming, data products, and different machine learning models solving optimisation, computer vision and LLM’s problems. This involves creating blueprints for data management systems to integrate, centralize, protect, and maintain the data sources.
• System Development: Lead the design and support the implemention of large-scale data processing systems capable of handling the volume, velocity, and variety of data that needs to be analyzed and processed.
• Data Strategy: Contribute and support of EGA’s data strategy, including the use of new technologies and practices, to improve the quality, reliability, and efficiency of data extraction and its use in machine learning and data product applications.
• Data Modelling & Management: Spearhead the creation and maintenance of conceptual, logical, and physical data models, supporting both operational and AI use cases. Oversee data integrity and quality assurance processes across upstream, midstream and downstream.
• Machine Learning Framework: Lead the design of the ML OPs framework and define Design workflows and processes that accelerate the development and deployment of machine learning models, ensuring that ML models are production-ready and scalable.
• Data Governance: Lead and contribute to define policies and procedures for data governance in collaboration with the data protection officer and other stakeholders, ensuring compliance with data privacy and protection laws. Lead efforts of data governance platform integration.
• Collaboration: Collaborate with IT teams, data engineers, and senior stakeholders to ensure that the data architecture supports and helps achieve strategic objectives. Lead in proposing, buy in and drive the end solution.
• Communicate effectively complex data concepts, solutions, proposals in simple language to non-technical team members and senior leaders (Senior Director,C level, Executive Committee).
• Mentorship and Leadership: Act as a mentor to data engineers (4-8), providing guidance and support in their professional development. Promote a culture of performance, collaboration, and continuous learning within the data team.
• Innovation and Continuous Improvement: Stay up-to-date with industry trends and new technologies. Continuously explore innovative solutions and enhancements to the existing data architecture to improve its scalability, reliability, and efficiency. Championing and driving initiatives like event sourcing, data contracts & schema registries, data lineage and automation downstream.
• Problem Solving: Drive solutions, anticipate and contribute to resolve technical issues before they become roadblocks, maintaining the continuity of data flow and ensuring the highest levels of data quality and integrity, I.e. alerting & monitoring strategy, change request frame work and automation, scaling up and data partitioning strategy
• Project Management: Coordinate and indirectly manage data-related projects (15-20 use cases every 3 month waves), ensuring they are completed on time and within budget, meeting or exceeding stakeholder expectations.

AUTHORITY/ DECISION MAKING:

• Design authority on data architecture, governance and technology selection to support scale up ambitions of 50-60 usecases a year.
• Validate data security protocols and resource allocation decisions based on usecase requirements.
• Validation of technical design and solutions to support 10 digital capabilities based on industry4.0 principles