Data Analyst

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