Software Engineering
About the job
Job Description
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Responsibilities
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 normalization 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.
Qualifications:
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
Equal employment opportunity information
KPMG India has a policy of providing equal opportunity for all applicants and employees regardless of their color, caste, religion, age, sex/gender, national origin, citizenship, sexual orientation, gender identity or expression, disability or other legally protected status.
KPMG India values diversity and we request you to submit the details below to support us in our endeavor for diversity. Providing the below information is voluntary and refusal to submit such information will not be prejudicial to you.
Qualifications
Engineering Grad / Postgraduate