AVP - Data Analytics - Automation Efficiency Quality Control - SAS/SQL - Banking Captive

AVP - Data Analytics - Automation Quality Control - SAS

Our client is a major Banking Captive based out in Bangalore

Role : AVP - Data Analytics - Automation Efficiency QC (Tools - SAS and SQL)

Experience : 9+ Years (Relevant)

Preferred Domain- Banking/Financial Services

Job Responsibilities (Generic) :

- Tools Required - SAS & SQL

- Good business awareness acquired over the last 10-15 years through a range of roles in line management and risk-related roles.

- Candidates having experience in data analytics, automation and reporting. Banking or financial domain is preferred.

- The job holder will be responsible for end to end project delivery and will have to co-ordinate multiple activities across the Global Risk and Data Reporting team effectively to ensure satisfactory delivery of the required results.

- Strong understanding of financial industry, strong project management abilities in delivering large and complex projects, Provide technical and business thought leadership to Risk Reporting and related services.

- Data Management, Business Intelligence Reporting and Infrastructure. Expected to design and develop Information Management Solutions which will help business drive informed decisions.

- Significant responsible in improving efficiency of the process via automation while focusing on RISK MI reporting including analysis internally and externally.

- Experience of data architecture, data integration, and enterprise architecture frameworks.

- Desire to understand the data landscape within Global Risk and the required Data & MI capabilities

- Deep knowledge of the information requirements of a large diversified Global bank, including relevant regulatory reporting requirements.

- Information on solutions which have been used elsewhere to deliver information & technological requirements similar to those of the Risk Function would be beneficial.

- Knowledge of best practices related to data governance and tools-based data quality improvement