Data Quality Analyst

Short Description:

With the growing push of data and information a Data Quality Analyst must be able to implement and monitor data quality checks, perform root cause analysis of DQ issues by running data queries and drive the solving process.

We developed new Data Quality Framework that contains the principles, processes as well as roles and responsibilities that are relevant to maintain, improve and manage data quality along the entire data processing chain ranging from local source systems to the target reporting systems within the bank.

To further expand and improve this scope we would like to onboard in the Data Quality team a new colleague passionate about Data with a growth mind and out of the box attitude, curious and ready to give a helping hand to improve the data quality within the bank.

Qualifications:

• To quickly pick up the role, it is useful that you have bachelor’s degree or master’s degree in economics / technical / mathematics / computer science/cybernetic;
• IT knowledge - SQL (on Oracle / other databases), Cognos, Power BI, advanced in Excel and Microsoft Office in general;
• Experience in Business Analysis, Data Quality or Data Management in Banking Industry is an advantage;
• Good banking knowledge (e.g. retail, corporate, data management, finance, reporting, controlling etc.);
• Team player with a positive attitude who embraces change;
• Analytic skills with correlation capacities and synthesis of information.

Job Responsibilities:

• Develop, maintain and govern conceptual, logical and physical data models and ensure consistency across workstreams;
• Perform analysis of new data feeds and map to existing data model;
• Design, test and maintain robust and scalable data models and ensure that data is correctly loaded into the system;
• Work with business specialists, understand processes and translate business specifications into technical requirements;
• Work together with developers, business analysts and product owners to ensure that new or modified models are designed and developed using industry standard best practice design and development practices;
• Proactively communicating data methodology, architecture, and results to a diverse set of stakeholders.