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Mashreq Global Services

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3.2

based on 321 Reviews

1 Mashreq Global Services Lead Analyst Job

Lead Analyst.Operations Analytics

Mashreq Bank

3.2

based on 321 Reviews

3-6 years

Kolkata, Mumbai, New Delhi + 4 more

1 vacancy

Lead Analyst.Operations Analytics

Mashreq Global Services

posted 17d ago

Job Role Insights

Flexible timing

Key skills for the job

Python Automation Testing Linux Administration Informatica SAP Business Intelligence MySQL

+ 3 more

Job Description

The Information Management stream delivers outcomes to help different parts of the bank gain meaningful insights from their Data Assets. Our core function spans multiple Data Warehouses, Hadoop Data Lakes, Data Mesh, Azure Cloud, Business Intelligence and Analytics within Information Management.

The purpose of a Data Engineer is to support the data engineering team in building and maintaining data pipelines, data platforms, databases, and other data infrastructure components. They are typically mid-level experience professionals who work under the are responsible for various tasks related to data processing, data management, application support and platform support.

3 to 6 Years of Total Experience in Software Development.
2+ years of experience Big Data, Hadoop, Data Lake, Microsoft SQL Server, Unix, and platform migrations or support
Programming Languages: Proficiency in at least one programming language commonly used in data engineering, such as Spark, Python, Java, Scala, or SQL. Python is particularly popular due to its versatility and extensive libraries for data processing.
SQL and Database Knowledge: Understanding of SQL (Structured Query Language) and familiarity with relational databases like MySQL, PostgreSQL, or Microsoft SQL Server. This includes the ability to write and optimize SQL queries.
Data Modeling: Knowledge of data modeling concepts, including designing and implementing data schemas for efficient data storage and retrieval.
Data Integration and ETL: Familiarity with Extract, Transform, Load (ETL) processes and tools used to integrate data from various sources into data warehouses or data lakes.
Data Cleansing and Preprocessing: Understanding of data cleaning techniques and data preprocessing to ensure data quality and consistency.
Big Data Technologies: Basic knowledge of big data technologies, such as Hadoop, Spark, or Apache NiFi, and their role in processing and managing large-scale data.
Version Control: Familiarity with version control systems like Git for collaborative development and code management.
Data Warehousing Concepts: Understanding the fundamentals of data warehousing, including data aggregation, star schema, and data cube concepts.
Data Formats: Knowledge of various data formats commonly used in data engineering, such as JSON, CSV, Parquet, Avro, etc.
Basic Cloud Computing: Familiarity with cloud computing platforms like AWS, Azure, or Google Cloud, and the ability to work with cloud-based data storage and processing services.
Linux/Unix Basics: Understanding of basic Linux/Unix commands and shell scripting for data processing and automation.
Data Visualization: Basic knowledge of data visualization tools like Tableau or Power BI to create visual representations of data.
Basic Domain Knowledge in Banking/Finance area.
Informatica power center is plus

Data Pipeline Development: Assisting in the design, development, and maintenance of data pipelines that extract, transform, and load (ETL) data from various sources into data warehouses, data lakes, or other storage systems.
Data Cleansing and Preprocessing: Supporting data cleaning and preprocessing tasks to ensure data quality and consistency before it is used for analysis or reporting.
Database Management: Assisting in the management and maintenance of databases, including setting up databases, creating tables, and ensuring data integrity.
Data Integration: Helping to integrate data from different sources, such as databases, APIs, and flat files, into a unified and coherent data structure.
Data Quality Assurance: Supporting efforts to ensure data accuracy and validity by performing data validation and data quality checks.
Documentation: Maintaining documentation of data engineering processes, data flows, and data dictionaries to facilitate knowledge sharing and future reference.
Data Monitoring and Troubleshooting: Assisting in monitoring data pipelines and infrastructure for any issues or errors and participating in troubleshooting efforts.
Learning New Technologies: Continuously learning and staying up-to-date with emerging data engineering tools and technologies.
Supporting Data Analysts and Data Scientists: Collaborating with data analysts and data scientists to provide them with access to the required data and assisting with data-related queries.
Assisting in Data Security Measures: Helping to implement data security measures, such as access controls and data encryption, to protect sensitive information.
Good in Coding and Scripting: Writing and maintaining scripts or code to automate data processing tasks.
Performance Optimization: Assisting in optimizing data processing and query performance to improve efficiency.
Data Backup and Recovery: Assisting in setting up data backup and recovery processes to ensure data resiliency.
Adhering to Best Practices: Following data engineering best practices and coding standards to maintain the quality and consistency of data processes.