Vice President- Senior Data Analyst

To be a successful Senior Business Analyst, you should have experience with:

1. Experience in delivering large-scale change in complex environments, acting as a thought leader in requirements documentation and workshop facilitation to gather, clarify, and communicate business needs effectively.
2. Strong data analysis, data modelling skills, capable of performing data validations, anomaly detection and making sense of large volumes of data to support decision-making.
3. Advanced SQL proficiency for querying, joining, and transforming data to extract actionable insights together with experience of data visualization tools (e.g. Tableau, Qlik, Business Objects).
4. Effective communicator, able to understand complex technical concepts and translate them into clear, accessible language for diverse audiences. Skilled in liaising between business stakeholders and technical teams to achieve a clear and mutual understanding of data interpretations, requirements definition and solution designs.
5. Experience working in Banking and Financial services, particularly in wholesale credit risk.
6. Background in implementing data governance standards, including metadata management, lineage, and stewardship.

Additional relevant skills given below are highly valued:

1. Experience with Python data analysis and associated visualisation tools
2. Familiarity with external data vendors for sourcing and integrating company financials and third-party datasets.
3. Experience with wholesale credit risk internal ratings-based (IRB) models and regulatory frameworks.

This role is for Chennai/Pune location as an Individual Contributor.

Purpose of the role

To implement data quality process and procedures, ensuring that data is reliable and trustworthy, then extract actionable insights from it to help the organisation improve its operation, and optimise resources.

Accountabilities

1. Investigation and analysis of data issues related to quality, lineage, controls, and authoritative source identification.
2. Execution of data cleansing and transformation tasks to prepare data for analysis.
3. Designing and building data pipelines to automate data movement and processing.
4. Development and application of advanced analytical techniques, including machine learning and AI, to solve complex business problems.
5. Documentation of data quality findings and recommendations for improvement.