Job Description

Role: Risk BI

Experience: 4–12 Years

Domain: Retail/Commercial Banking (Lending, Cards, Deposits, Risk, Collections, Regulatory)

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 & Risk KPIs.

Overview:

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 Adhoc Regulatory Requirements: Ensure timely delivery of adhoc regulatory requirements from the risk team.

Risk Data Mart Creation and Management: Design, develop and maintain a central repository of all risk features that gets repeatedly used by various users within the risk team. Risk data mart will ensure re-usability of data elements without duplication of efforts by each users 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.

1. Data Engineering & Pipelines

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

1. 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.

1. 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.

1. Banking Domain Expertise

• Build KPIs, dashboards, and insights around:

o Portfolio performance

o NPA/ECL metrics

o Roll rates & vintage curves

o Acquisition funnels

o Regulatory/management reporting

• Ensure metric standardization, data dictionary management, and governance alignment.

1. 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 & 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 & 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