Data Management Lead

• Communicate with many different company stakeholders to assess needs and goals
• Evaluate current data architecture and databases
• Plan end-to-end model for data consumption and analysis
• Code new data architecture, integrating existing databases
• Use ETL (extract, transform, load) best practices in the creation of the database
• Research and discover new methods to acquire data
• Monitor databases and report on effectiveness and accuracy, updating as needed
• Maintain security of the database, and create standards to ensure that the system is safe
• Preserve a repository of all data architecture artifacts
• Review Functional Specifications;
• Assist in User Acceptance Test and Live simulation
• Ensure system embedded controls are in place;
• Ensure that the access rights matrix is validated by Aheza Steerco;
• Elaborate the Training needs analysis;
• Assist in the elaboration of training materials;
• Conduct training

• Experience of 4-year minimum working as Database Administrator or Database Reporting in Banking Sector (Regulatory Electronic Data Warehouse)
• Ability to quickly learn new technologies, think logically and solve problems independently.
• Development experience in a Cross-platform, multi-tier environment
• Ability to think analytically and logically; and work independently or as part of a team
• Ability to explain complex technical issues to non-technical business personnel.
• Ability to work autonomously and under pressure;
• Good communication and interpersonal skills.

• Bachelor’s Degree in Computer Science, computer engineering, information systems or any other relevant degree.

• Knowledge in IT software and hardware
• Innovation ability
• Knowledge of relevant financial services and IT legislation,
• Knowledge of professional standards
• Applied math and statistics
• Data visualization and data migration
• RDMSs (relational database management systems) or foundational database skills
• Database management system software, especially Microsoft SQL Server
• Databases such as NoSQL and cloud computing
• Hadoop technologies, like MapReduce, Hive, and Pig
• information management and data processing on multiple platforms
• Machine learning
• Data mining and modeling tools, especially ERWin, Enterprise Architect, and Visio
• Programming languages, especially Python and Java, as well as C/C++ and C#
• Operating systems, including UNIX, Linux, Solaris, and MS Windows
• Backup/archival software