Data Analyst (Python)

Role & responsibilities

Functional Skills

• Determining, creating, and implementing internal process improvements, such as redesigning infrastructure for increased scalability, improving data delivery, and automating manual procedures.
• Building analytical tools that make use of the data flow and offer a practical understanding of crucial company performance indicators like operational effectiveness and customer acquisition.
• Helping stakeholders, including the data, design, product, and executive teams, with technical data difficulties.
• Working on data-related technical challenges while collaborating with stakeholders, including the Executive, Product, Data, and Design teams, to support their data infrastructure needs.
• Remaining up-to-date with developments in technology and industry norms can help you to produce higher-quality results.

Technical Skills:

• Analyze large datasets to derive actionable insights and support decision-making processes.
• Develop and maintain data pipelines using PySpark and other data processing tools.
• Write efficient SQL queries to extract, transform, and load data from various sources.
• Implement data models and schemas to organize and optimize data storage and retrieval.
• Perform data norplmalization and denormalization to ensure data integrity and accessibility.
• Collaborate with data engineers to centralize and manage data assets.
• Ensure data quality through validation and cleansing processes.
• Utilize CI/CD pipelines to streamline data deployment and maintain continuous integration.

Preferred candidate profile

Qualifications:

• Min 4 Years of Proven experience in data analytics and working with large datasets.
• Proficiency in Python, including libraries such as Pandas and Numpy for data manipulation.
• Strong SQL skills for querying and managing databases.
• Experience with PySpark for large-scale data processing.
• Basic understanding of Hadoop and its ecosystem.
• Familiarity with data engineering concepts and best practices.
• Knowledge of data modeling, including schemas, normalization, and denormalization techniques.
• Understanding of data centralization, cardinality, and data quality principles.
• Good to have experience in CI/CD pipelines and tools.

Banking

• Deep understanding of banking operations, financial products, and regulatory frameworks
• Experience with data modeling, ETL processes, and statistical analysis
• Prior experience in retail or corporate banking analytics
• Analyze banking data including customer transactions, loan performance, and financial statements
• Support credit risk analysis and fraud detection initiatives
• Maintain and optimize banking databases and data pipelines