WHAT YOU NEED TO KNOW (OR TECHNICAL SKILLS)

• Solid, proven background in data modelling, analysis and mapping to business

capabilities and applications.

• Foundational knowledge of Enterprise Architecture, Domain Driven Design principles, systems development, application design and information management.
• Experience with event-based, lambda and hybrid deployment (i.e., cloud and on-premises) data architectures, and understanding of a broad range of data-oriented technologies and their applicability.
• Experience with one or more data warehouse platforms (preferably Databricks and Spark).
• Understanding of the principles, practices and techniques of Data Engineering and Machine Learning.
• Working knowledge of one or more data-oriented programming languages (e.g., Python, Java), visualization techniques and analytics tools.

YOU (BEHAVIOURAL SKILLS)

• Business acumen - Create effective designs and policies, aligned with strategic needs of the organization and considering opportunities, risks and uncertainties.
• Communication skills - Make complex, technical information and language simple and accessible. Listen and interpret the needs of business and technical stakeholders and

manage expectations. Support or host difficult discussions with SME’s and senior stakeholders.

• Partnership skills - Building relationships and leading through influence.
• Presentation skills - Confident and methodical, creating clear, well-understood analysis, reports and solution designs that can be acted upon
• Analytical and numerical skills - Undertake data profiling and analysis, deriving accurate insights from large volumes data with complex relationships.
• Critical thinking and problem-solving skills - anticipate and mitigate against problems at the right time, able to understand and articulate how the issue fits and impacts the larger picture.

requirements into clear designs and processes for data production, management, and consumption.

• Acting as a key stakeholder and advisor in complex strategic initiatives, ensuring alignment across the organization and that design is in accordance with the strategy.
• Overseeing the governance and high-level design of multiple data models, developing a broad understanding of their relationships and how each fulfills the needs of the organization.
• Working in close collaboration with architects, engineers, and product partners across the enterprise to provide technical oversight, leadership and consultation on data relevance, structure, and lifecycle.
• Actively contributing toward data-related security, compliance, and privacy practices, ensuring they are embedded in the strategy.
• Setting and advocating standards, principles, and ways of working for the broader Data Architecture community.
• Building and maintaining appropriate artifacts to support communication and alignment to the strategy.
• Developing key performance indicators to assure integrity, confidence, and quality of data.
• Assessing technology and regulatory trends in the wider data landscape, how they may impact opportunities and timing for adoption

• Accountable for Ticketmaster’s Data Architecture strategy, roadmap, and policies, aligned with our long-term technology and business goals.
• Building effective relationships with business and product partners, translating

requirements into clear designs and processes for data production, management, and consumption.

• Acting as a key stakeholder and advisor in complex strategic initiatives, ensuring alignment across the organization and that design is in accordance with the strategy.
• Overseeing the governance and high-level design of multiple data models, developing a broad understanding of their relationships and how each fulfills the needs of the organization.
• Working in close collaboration with architects, engineers, and product partners across the enterprise to provide technical oversight, leadership and consultation on data relevance, structure, and lifecycle.
• Actively contributing toward data-related security, compliance, and privacy practices, ensuring they are embedded in the strategy.
• Setting and advocating standards, principles, and ways of working for the broader Data Architecture community.
• Building and maintaining appropriate artifacts to support communication and alignment to the strategy.
• Developing key performance indicators to assure integrity, confidence, and quality of data.
• Assessing technology and regulatory trends in the wider data landscape, how they may impact opportunities and timing for adoption.