Data Analyst Permanent

RESPONSIBILITIES:

1. End-to-end design and build of in-scope data quality rules within Alteryx Tool
   1. Defining configurable input sources for data extraction and cleansing
   2. Defining the best-fit approach for a given data quality rule through usage of dynamic workflows
   3. Use of staged workflows for e2e report generation in an automated manner
   4. Standardised output format across data quality rules
2. Testing and validation of results
   1. Create standardized test data set
   2. Validate the results using test set
3. Clear documentation of design and workflows
   1. Create documentation of overall design, configurable input data and workflows with reporting logic
   2. Storage of artefacts including testing results
4. Adherence to laws and regulations, internal risk and compliance policies ethics and the Group Code of Conduct
5. Completion of all mandatory risk and compliance training including e-learning within stated timeframes
6. Safeguard all data by ensuring strict adherence to information communication guidelines polices as well as data storage/archival procedures

REQUIREMENTS/KNOWLEDGE

Qualifications and technical competencies

• 5+ years of experience in data analysis using SQL or Business Intelligence Tools
• Demonstrated expertise of data analysis skills using Alteryx
• Experience working with large data sets and relational databases
• Proficient in SQL and/or other data query skills
• Knowledge of data quality management will be added plus
• Knowledge of R or Python will be added plus
• Degree or diploma in Computer Applications/ Information Systems/ relevant subjects
• Experience in banking / financial institution preferred

Skills

• Strong analytical skills and logical approach to problem solving
• Good verbal and written communication skills
• Ability to work effectively as a self-starter with self-motivation, as well as good organisation skills, and ensure deadlines are met.
• Proven ability to collaborate effectively with stakeholders and engage with all levels of management
• Good understanding of the risks related to poor data.
• Innovative mind-set that always looks for automated solutions rather than manual work around