Data Analyst

Job 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.