Risk Manager

Desired strong expertise in SQL, Power BI, Python, Airflow, and CI/CD pipelines, combined with hands-on Data Engineering capabilities. The role involves designing and delivering enterprise-grade BI solutions, automating data pipelines, and enabling data-driven decision making across key banking portfolios. Should be well versed with Risk background and Risk KPIs.

To lead and manage business intelligence initiatives with a strong emphasis on automation, data democratization, standardization, and governance. The role requires expertise in data modelling, data management, and dashboard-driven insights. It ensures timely and high-quality inputs from a data design and architecture perspective, facilitating the storage of customer data from various sources, with a primary focus on aiding risk management.

Key Responsibilities:

• Automate MIS and reporting: Ensure all Management Information Systems (MIS), reports, and risk dashboards are automated and accessible to stakeholders through Power BI or other visualization tools.
• Develop action-oriented dashboards: Create multiple dashboards with actionable insights for risk monitoring using various AI tools.
• Organize data efficiently: Structure data at the customer and product levels to facilitate easy analysis and transaction-level insights and aid hindsighting.
• Bureau reporting: Manage and automate bureau reporting processes.
• Prepare review decks: Compile various decks for timely reviews by various stakeholders, including CRO's office, board committees, and top management.
• Automate regulatory reports: Streamline the automation of various regulatory reports.
• Deliver ad hoc regulatory requirements: Ensure timely delivery of ad hoc regulatory requirements from the risk team.
• Risk data mart creation and management: Design, develop, and maintain a central repository of all risk features that get repeatedly used by various users within the risk team. The risk data mart will ensure re-usability of data elements without duplication of effort, generating huge efficiencies.
• Collaborate across teams: Work closely with risk, compliance, product teams, IT, data engineers, and other governance teams to ensure consistent data management.
• Generate collection data insights: Create insights from collection data for use by the risk team for various products.

Business Intelligence & Reporting:

• Build and maintain enterprise dashboards and reports using Power BI (DAX, data modelling, Power Query).
• Translate business requirements into analytical datasets, KPIs, and visualizations for business, risk, and finance groups.
• Manage BI lifecycle: design -> development -> testing -> deployment -> support.

Data Engineering & Pipelines:

• Design and orchestrate scalable ETL/ELT pipelines using Apache Airflow.
• Integrate data from DWH and other core banking systems, credit bureaus, and transaction systems.
• Ensure data quality, lineage tracking, schema evolution, and data governance compliance.

SQL & Data Modeling:

• Write complex SQL queries, improve query performance, create stored procedures, and manage large-scale banking datasets.
• Build fact/dimension models for analytics consumption layers.
• Implement incremental refresh and optimize semantic models for Power BI.

CI/CD & DevOps for Data:

• Implement CI/CD pipelines for BI and data workflows (Git, Azure DevOps, GitHub Actions, etc.).
• Automate deployment of Power BI assets, SQL scripts, Airflow DAGs, and Python code across dev/uat/prod.
• Establish version control, code review processes, unit testing, and release governance.

Banking Domain Expertise:

• Build KPIs, dashboards, and insights around:
• Portfolio performance
• NPA/ECL metrics
• Roll rates and vintage curves
• Acquisition funnels
• Regulatory/management reporting
• Ensure metric standardization, data dictionary management, and governance alignment.

Leadership & Collaboration:

• Lead BI developers/data analysts in delivering high-quality outputs.
• Conduct code reviews, enforce best practices, and ensure on-time delivery.
• Drive data and BI modernization initiatives across the bank.

Required Technical Skills:

• SQL (Advanced): Indexing, query optimization, stored procedures.
• Power BI (Expert): Data modelling, DAX, RLS, incremental refresh, composite models, deployment pipelines.
• Python: pandas, numpy, SQL connectors, automation scripts, scheduling.
• Airflow: DAG design, custom operators, task orchestration, SLAs, retry logic.
• CI/CD: Automated deployments, code quality frameworks.
• Data Engineering: ETL/ELT design, data modeling, data validation frameworks, structured and semi-structured data handling.
• Experience with relational databases (SQL Server, Oracle, Postgres, MySQL).

Preferred Skills:

• Exposure to cloud data platforms (Azure Data Factory/Synapse, AWS Redshift/Glue, GCP BigQuery).
• Knowledge of Tableau / Power BI Admin features.
• Understanding of risk analytics metrics and regulatory reporting.

Qualifications:

• Bachelor's or Master's degree in Engineering, Computer Science, Information Systems, Statistics, or equivalent.
• Banking BI/Data experience is mandatory.

Success Indicators:

• Highly reliable and automated BI + data pipelines
• Strong data quality and governance adherence
• High adoption of dashboards across Risk teams
• Consistent on-time delivery of insights and pipelines