Key Accountabilities

Description

• Exploiting the value of enterprise data assets and analytics: Take authority, responsibility and accountability for exploiting the value of enterprise data assets, and of the analytics used to render insights for decision making, automated decisions and augmentation of human performance. Be the organization leader of data-driven insights that help support the exploitation of strategic and tactical business opportunities. Exploit data using research and analytics to maximize the return on data assets. Develop methods to ensure consistent application and use of analytics. Establish the governance of data and algorithms used for analysis, analytical applications and automated decision making. Creates value by unlocking and sharing data and information in ways which will spur innovation
• Define and oversee the implementation of Mubadala Data & Analytics Vision and Strategy: Work with Data Governance Council members and other senior executives to establish the vision for managing data as a business asset. Define data and analytics strategy practices, lead the creation (and assure the ongoing relevance) of the organization’s data and analytics strategy in collaboration with Mubadala senior stakeholders. Institute an enterprise operating model that is consistent with the capabilities and competencies required to execute the strategy
• Identifying and Defining Data & Analytics Investments: Works with business and ETS leaders to identify areas of technical needs for future data analytics capabilities. Drives the development and deployment of the data and analytics platform.  Identifies and prioritizes business projects and enterprise data initiatives utilizing data science capabilities. Lead research, strategy creation and development of new data solutions to monetize data (directly and indirectly) and grow company revenue
• Expand Mubadala’s research and analytics offerings: Expand the organization’s research and analytics offerings, especially in emerging analytical approaches, skills and technologies, focusing them on digital business innovation
• Creation of a Data Driven Culture: Foster the creation of a data-driven culture, related competencies and data literacy across the organization
• Enable business innovation through data: Identify new kinds, types and sources of data to enable business innovation throughout the organization. Create and oversee a centralized service for sourcing external data to ensure quality, traceability, timeliness, usability and cost-effectiveness. Define processes for the effective, integrated introduction of new data
• Establishing Data Governance: Cascades from data strategy into data governance to promote data access, quality, and analytical capability.  This includes developing and implementing a knowledge worker training program, a data stewardship model, and data standards.  Develops and maintains controls on data quality, interoperability, and sources to effectively manage risk associated with the use of data and analytics. Strives to reduce the cost of managing data and increase the value of the data. Define, manage and ensure an adequate information trust model, controls for master data and metadata management, including reference data.
• Management and Operational Accountabilities: Develop, manage, allocate and govern the annual budget for Enterprise Data Management program. Organize and lead a data and analytics center of excellence, and constantly improve the organization’s capacity to develop insights with advanced analytics. Define members’ responsibilities and accountabilities for both. Define job roles, recruit candidates, and then manage (directly or indirectly) data and analytics team. Lead the development, publishing and maintenance of the organization’s data architecture, as well as a roadmap for its future development, ensuring that it matches and supports business needs. Oversee the integration and staging of data, and the development and maintenance of the data lakes, data warehouse and data marts, for use by analysts throughout the organization. Oversee the implementation of EDM related initiatives and programs. Serves as the Data Analytics technical advisor.  Represents the D&A team and enterprise data stewards at leadership meetings.  Develops and gives oral presentations on the power and value of data.  Leads and/or serves on Mubadala Data Governance Council and helps leadership use its data effectively at these meeting

Qualifications and Experience

Minimum Qualifications:

• A bachelor’s or master’s degree in business administration, computer science, data science, information science or related field, or equivalent work experience. Academic qualification or professional training and experience in data regulatory areas or financial domain are also desirable.

Experience:

• At least 15 years of progressively responsible relevant experience in data management ideally in a Private Equity, Investment Company or financial institution, including creating partnerships, implementing data governance, and understanding the underlying technologies needed to enable data innovation across a large organization.
• Recently at or near the executive level. Broad business experience internally and within the vertical industry is desired
• Experience in computer programming, query languages, and data visualization platforms is also preferred
• Demonstrated experience with developing data strategy, policies, and procedures, as well as successfully executing programs that meet or exceed expectations in a dynamic environment; experience creating tools and capabilities to assist with data discovery & collaboration, ensure data quality, and to load, clean, enrich, manage, and share data and metadata from a variety of sources
• Familiar with big data technologies (e.g. Hadoop, HBase, Lucene/Solr) and Extract Transform Load (ETL) tools

Knowledge:

• Ability to define strategic initiatives and oversee the development of long-term plans and proposals
• Ability to effectively drive business, culture, and technology change in a dynamic and complex operating environment
• Ability to effectively coordinate, allocate, and manage resources and projects throughout multiple teams
• Knowledge of trends and developments in the fields of data management and data analytics
• Data management and quantitative skills, including working knowledge of IT infrastructure, various technologies/platforms, and enterprise-specific vendor solutions
• Industry-specific knowledge of data security protocols, policies, and regulations
• Exceptional analytical, written, oral, and presentation skills with proven track record of effectively communicating actionable insights
• Exemplary diplomatic skills and ability to help senior business leaders see value, opportunities, and risks beyond their own area of expertise
• Ability to understand problems from a broad perspective and anticipate the impact of administrative issues and solutions
• Excellent investment business acumen and interpersonal skills; able to work across business lines at a senior level to influence and effect change to achieve common goals.
• Demonstrated leadership; proven track record of leading complex, multidisciplinary talent teams in new endeavors and delivering solutions.
• Proven data literacy — The ability to describe business use cases/outcomes, data sources and management concepts, and analytical approaches/options. The ability to translate among the languages used by executive, business, IT and quant stakeholders.
• Ability to effectively drive business, culture and technology change in a dynamic and complex operating environment.
• Ability to develop a framework for data and analytics governance, as well as to sell and embed it in all levels of the business.
• Proven record of effective leadership, including the ability to balance team and individual responsibilities, build teams and consensus, get things done through others not directly under his/her supervision, and work ethically and with integrity.
• Demonstrated knowledge of investment or public equity industry-specific’s business processes and resultant data needs.
• Demonstrated knowledge of the following is desired, but not essential: knowledge management, contact relationship management, data structure, information systems/tools, related software and data management, enterprise content management, and record-keeping policies and practices in a complex organizational environment.
• Broad experience desired, but not essential, in multiple competency areas of data and analytics delivery. Examples are data warehousing, business intelligence (BI), data governance, data architecture, data integration, data classification, data strategy, data quality management, data security and privacy, MDM, data standards, regulatory compliance and enterprise architecture frameworks.

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