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Mid-level popular data analyst role with common SQL/PySpark skills but some BFSI specialization reduces applicant density.
Requires BFSI credit and lending domain knowledge, making cross-industry transferability limited.
Explicit 5+ years requirement plus mandatory SQL/PySpark and domain knowledge enforces strict filtering.
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Own the end-to-end delivery of data analysis projects in the BFSI domain, focusing on credit and lending.
Convert business problems into analytical solutions using SQL, PySpark, Python, and Big Data tools like Hadoop, Hive, and Spark.
Collaborate on data asset design, mapping, and data quality validation to enable data-driven decision-making and process efficiency gains.
5+ years professional experience as a Data Analyst, preferably with banking domain exposure in credit and lending.
Proficient in SQL, PySpark, Python, and Big Data frameworks such as Hadoop, Hive, and Spark.
Graduate degree in Computer Science, Data Science, or related field.
Experience with credit risk frameworks (Basel II, III, IFRS 9, Stress Testing) is advantageous but not mandatory.
Strong domain expertise in BFSI with the ability to translate complex business requirements into technical data models and solutions.
Experience working in Agile environments with a focus on project delivery and stakeholder communication.
Skilled in data architecture discussions and able to work closely with team leads to refine solution design and delivery.