Associate - Data Analytics - Internal Audit Domain - BFSI

Python+SQL+Visualization tools+Analytics+Unix

Minimum of 2 years- working experience of programming in Python and familiar with Linux/Unix environment. Alternatively, a minimum of 2 years' experience developing databases or implementing data marts and writing SQL queries to analyze large volumes of data will also be considered. Furthermore, a keen interest in learning programming skills in new technologies (e.g. Elastic Search, Graph Technology, TensorFlow, etc.) is required

- Experience of data analysis and mining tools and languages, including SQL Server, MongoDB, Graph, SSIS, Informatica, Python, R. Practical experience of using machine learning algorithms (e.g. Pandas: Python Data Analysis Library) would be seen as an advantage. Prior experience using searching technologies such as Elastic search would be seen as an advantage

- Prior experience in a data analytics role with hands-on experience working with data.

- Experience with visualization technologies such as Qlikview/Qlik Sense or Tableau, specifically the ability to stage data from multiple sources, design dashboard content, and develop applications through multiple iterations with users

- Experience of system development (build, design and/or implementation), preferably in an Investment Bank/Broker dealer or Financial Services, or with Big 4 firm accountancy firm

- Excellent oral and written communication skills

- Good analytical and decision-making skills.

- Self-starter and a team player who enjoys working in a global and diverse environment.

- Able to multi-task and work to tight deadlines

- Good understanding of Project Management and Systems Development processes

- Good understanding of business processes and technology supporting those processes

- Experience of auditing technology risk and controls, preferably within Investment Banking or Financial Services is an advantage

- Understanding IT general controls, including system security, data integrity and system Architecture & design - Data Integrity (scalability, performance, resilience & availability)