Data Analyst

Must-Have

• 4+ / 5+ years of professional experience as a Data Analyst with good decision-making, analytical and problem-solving skills.
• SQL, Pyspark, Python with Banking Domain knowledge - Credit & Lending.

Working knowledge / experience of Big Data frameworks like Hadoop, Hive and Spark.

• Hands-on experience in query languages like HQL or SQL (Spark SQL) for Data exploration.

Data mapping

Determine the data mapping required to join multiple data sets together across multiple sources.

• Documentation - Data Mapping, Subsystem Design, Technical Design, Business Requirements.
• Exposure to Logical to Physical Mapping, Data Processing Flow to measure the consistency, etc.

Data Asset design / build

Working with the data model / asset generation team to identify critical data elements and determine the mapping for reusable data assets.

• Understanding of ER Diagram and Data Modelling concepts
• Exposure to Data quality validation
• Exposure to Data Management, Data Cleaning and Data Preparation
• Exposure to Data Schema analysis.
• Exposure to working in Agile framework.
• Knowledge of Credit Risk Frameworks such as Basel II, III, IFRS 9 and Stress Testing and understanding their drivers - advantageous

Must-Have

• 4-6 years of professional experience as a Data Analyst with good decision-making, analytical and problem-solving skills.
• SQL, Pyspark, Python with Banking Domain knowledge - Credit & Lending.

Working knowledge / experience of Big Data frameworks like Hadoop, Hive and Spark.

• Hands-on experience in query languages like HQL or SQL (Spark SQL) for Data exploration.

Data mapping

Determine the data mapping required to join multiple data sets together across multiple sources.

• Documentation - Data Mapping, Subsystem Design, Technical Design, Business Requirements.
• Exposure to Logical to Physical Mapping, Data Processing Flow to measure the consistency, etc.

Data Asset design / build

Working with the data model / asset generation team to identify critical data elements and determine the mapping for reusable data assets.

• Understanding of ER Diagram and Data Modelling concepts
• Exposure to Data quality validation
• Exposure to Data Management, Data Cleaning and Data Preparation
• Exposure to Data Schema analysis.
• Exposure to working in Agile framework.
• Knowledge of Credit Risk Frameworks such as Basel II, III, IFRS 9 and Stress Testing and understanding their drivers - advantageous

Graduate in Computer Science, Data Science, or related field. 2-3 years of experience in data engineering or related field.