EXL Data Analyst (Banking)

Role: Data Analyst
Work Mode: Hybrid
Experience: 4+ Years

Must-Have Skills

- 4+ years of professional experience as a Data Analyst with strong decision-making, analytical, and problem-solving skills.
- Solid knowledge of SQL, PySpark, Python, with Banking Domain experience (Credit & Lending).
- Working knowledge of Big Data frameworks such as Hadoop, Hive, and Spark.
- Hands-on experience with query languages such as SQL, HQL, and Spark SQL for data exploration.
- Experience in Data Mapping to join multiple datasets across multiple data sources.
- Experience preparing documentation:
- Data Mapping
- Subsystem Design
- Technical Design
- Business Requirements

- Exposure to:

- Logical to Physical Data Mapping
- Data Processing Flow
- Data Consistency Measurement

- Experience in Data Asset Design/Build, collaborating with data model and asset generation teams to identify critical data elements and reusable data assets.
- Understanding of ER Diagrams and Data Modeling concepts.
- Exposure to:

- Data Quality Validation
- Data Management
- Data Cleaning
- Data Preparation
- Data Schema Analysis

- Experience working in an Agile environment.
- Knowledge of Credit Risk Frameworks such as Basel II, Basel III, IFRS 9, and Stress Testing is an advantage.

Responsibilities

- Convert business problems into analytical problems and identify appropriate solutions.
- Demonstrate overall business understanding of the BFSI domain.




- Provide high-quality analysis and recommendations for business problems.
- Ensure efficient project management and timely delivery.
- Conceptualize data-driven solutions for multiple businesses/regions to support decision-making.
- Drive process improvements and operational efficiency.
- Use data insights to improve customer outcomes.
- Understand business requirements from product/project stakeholders and break them into user stories and tasks.
- Map business entities to technical attributes with clearly defined transformation logic.
- Deliver assigned tasks within defined timelines while maintaining quality.
- Collaborate closely with team leads to ensure smooth project delivery.
- Understand and define solution architecture, discussing pros and cons with the team.
- Participate in brainstorming sessions and recommend architecture/design improvements.
- Work with other technical leads to review architecture and design.
- Keep stakeholders informed about project status, risks, issues, and task progress.

Qualifications

- Graduate in Computer Science, Data Science, or a related field.
- 2–3 years of experience in Data Engineering or a related field.

Note: The document mentions "Experience: 4+ Years" at the top, while the qualification section states "2–3 years of experience in Data Engineering or related field." This appears to be an inconsistency in the source document.

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