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.