Mubadala Investment Company Jobs and Careers | Abu Dhabi | UAE

Job Purpose

Defines the data and analytics strategy and data governance program for Mubadala. Leads in the planning, evaluating, and selecting the right data analytics capabilities and technologies for the enterprise. Serves as an advisor to the leadership team to develop and deliver enterprise-wide solutions for complex data challenges, including developing and applying algorithmic models to derive insights, improve decision making, and automate processes. Bears responsibility on behalf of Mubadala to foster value creation by means of the organization’s data assets and external data ecosystem. This includes:

• Creating value through data exploitation
• Envisioning data-enabled strategies
• Enabling all forms of business outcomes through analytics
• Establishing data and analytics governance

• Specifying enterprise data related policies

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.

Apply Now

Job Purpose

Plays a pivotal role in building and operationalizing the minimally inclusive data necessary for data and analytics initiatives following industry standard practices and tools. Builds, manages and optimizes data pipelines and then moves these data pipelines effectively into production for key data and analytics consumers that need curated data for data and analytics use cases across Mubadala. Acts as the key interface in operationalizing data and analytics on behalf of Mubadala units and organizational outcomes. Works with key business stakeholders, IT technical teams and subject-matter experts to plan and deliver optimal analytics and data science solutions.

Key Accountabilities

• Build data pipelines: Architect, create, maintain and optimize data pipelines as workloads move from development to production for specific use cases through a series of stages (e.g from data sources or endpoints of acquisition to integration to consumption for specific use cases).
• Drive Automation through effective metadata management: The data engineer will be responsible for using innovative and modern tools, techniques and architectures to partially or completely automate the most-common, repeatable and tedious data preparation and integration tasks in order to minimize manual and error-prone processes and improve productivity. The data engineer will also need to assist with renovating the data management infrastructure to drive automation in data integration and management. This will include (but not be limited to) learning and using modern data preparation, integration and AI-enabled metadata management tools and techniques, tracking data consumption patterns, performing intelligent sampling and caching, monitoring schema changes, recommending — or sometimes even automating — existing and future integration flows.
• Collaborate across Units: The data engineer will need strong collaboration skills in order to work with varied stakeholders within the organization. In particular, the data engineer will work in close relationship with data science teams and with business analysts in refining their data requirements for various data and analytics initiatives and their data consumption requirements.
• Educate and train: The data engineer should be knowledgeable about new data initiatives and how to address them. This includes applying their data and/or domain understanding in addressing new data requirements. They will also be responsible for proposing appropriate (and innovative) data ingestion, preparation, integration and operationalization techniques in optimally addressing these data requirements. The data engineer will be required to train counterparts such as data scientists, data analysts or any data consumers in these data pipelining and preparation techniques, which make it easier for them to integrate and consume the data they need for their own use cases
• Participate in ensuring compliance and governance during data use: It will be the responsibility of the data engineer to ensure that the data users and consumers use the data provisioned to them responsibly through data governance and compliance initiatives. Data engineers should work with data governance teams and data champions and participate in vetting and promoting content created in the business and by data scientists to the curated data catalog for governed reuse.
• Become a data and analytics evangelist: The data engineer will be considered a blend of data and analytics “evangelist,” “data guru” and “fixer.” This role will promote the available data and analytics capabilities and expertise to business unit leaders and educate them in leveraging these capabilities in achieving their business goals

• A bachelor’s degree in computer science, statistics, applied mathematics, data management; information systems, information science or a related quantitative field is required.
• A master’s degree in computer science (MS), statistics, applied mathematics, information science (MIS), data management; information systems, information science or a related quantitative field is preferred.
• The ideal candidate will have a combination of IT skills, data governance skills, and analytics skills with a technical or computer science degree.

Experience:

• At least six years or more of work experience in data management disciplines including data integration, modelling, optimization and data quality, and/or other areas directly relevant to data engineering responsibilities and tasks.
• At least three years of experience working in cross-functional teams and collaborating with business stakeholders in support of a unit and/or multi-unit data management and analytics initiative.

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
• Strong experience with various Data Management architectures like Data Warehouse, Data Lake, Data Hub and the supporting processes like Data Integration, Governance, Metadata Management
• Strong ability to design, build and manage data pipelines for data structures encompassing data transformation, data models, schemas, metadata and workload management.
• Strong experience in working with large, heterogeneous datasets in building and optimizing data pipelines, pipeline architectures and integrated datasets using traditional data integration technologies. These should include ETL/ELT, API design and access and upcoming data ingestion and integration technologies such as data virtualization.
• Basic experience in working with data governance/data quality and data security teams and specifically information stewards and privacy and security officers in moving data pipelines into production with appropriate data quality, governance and security standards and certification.
• Demonstrated success in working with large, heterogeneous datasets to extract business value using popular data preparation tools to reduce or even automate parts of the tedious data preparation tasks.
• Strong experience with popular database programming languages including SQL, PL/SQL, others for relational databases and certifications on Hadoop oriented databases.
• Strong experience in working with SQL on Hadoop tools and technologies.
• Strong experience with advanced analytics tools for Object-oriented/object function scripting.
• Strong experience in working with both open-source and commercial message queuing technologies such as Kafka.
• Ability to automate pipeline development – Strong experience in working with DevOps capabilities like version control, automated builds, testing and release management capabilities.
• Strong experience in working with data science teams in refining and optimizing data science and machine learning models and algorithms
• Demonstrated success in working with both IT and business while integrating analytics and data science output into business processes and workflows.
• Basic experience working with popular data discovery, analytics and BI software tools like Tableau, Microstrategy, PowerBI and others for semantic-layer-based data discovery.
• Basic understanding of popular open-source and commercial data science platforms such as Python and others is a strong plus but not required/compulsory.
• Demonstrated ability to work across multiple deployment environments including cloud, on-premises and hybrid] multiple operating systems and through containerization techniques.
• Adept in agile methodologies and capable of applying DevOps and increasingly DataOps principles to data pipelines to improve the communication, integration, reuse and automation of data flows between data managers and consumers across an organization
• Strong experience supporting and working with cross-functional teams in a dynamic business environment.
• Required to be highly creative and collaborative. An ideal candidate would be expected to collaborate with both the business and IT teams to define the business problem, refine the requirements, and design and develop data deliverables accordingly. Required to have the accessibility and ability to interface with, and gain the respect of, stakeholders at all levels and roles within the company.

Apply Now