Lead Assistant Manager

EXL

. Convert business problems into analytical problems and identify pertinent solutions .

Posted 10/15/2026full-timeBengaluru • IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates strong analytical and problem-solving capabilities, with expertise in SQL, PySpark, and Python for data analysis in the Banking domain. Proficient in data mapping, data modeling, and delivering high-quality insights to support decision-making and process enhancements.

Highest-signal resume keywords

SQLPySparkPythonData MappingBanking Domain Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data AnalysisData ModelingData Quality ValidationData PreparationData CleaningHQLSpark SQLLogical-to-Physical MappingData Asset GenerationER Diagrams

Soft Skills

Decision-MakingAnalytical SkillsProblem-SolvingProject ManagementCommunication

Tools & Technologies

HadoopHiveSparkJupyter NotebookJiraConfluenceMicrosoft ExcelCloud Platforms (GCP, AWS, Azure)

Industry Keywords

BFSICredit & LendingCredit Risk FrameworksBasel IIBasel IIIIFRS 9Stress TestingAgile Frameworks

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformHadoopPySparkPythonSparkSQL

About the role

Key responsibilities & impact

Convert business problems into analytical problems and identify pertinent solutions
Apply BFSI domain understanding to business problems
Provide high-quality analysis and recommendations
Manage projects and deliver work efficiently
Conceptualize data-driven solutions for multiple businesses or regions to support decision-making
Drive efficiency gains and process enhancements
Use data to improve customer outcomes through insight and challenge
Understand business requirements from product and project stakeholders
Break requirements into stories and tasks and map tasks to logical solution models
Map business entities to technical attributes and define transformation logic
Deliver assigned tasks within defined timelines and quality standards
Work closely with team leads to support project delivery
Understand and define architecture and discuss its pros and cons
Participate in brainstorming and suggest architecture or design improvements
Work with other team leads to review architecture and design
Keep stakeholders updated on project and task status, risks, and issues

Requirements

What you’ll need

5+ years of professional experience as a Data Analyst with good decision-making, analytical and problem-solving skills
Strong SQL, PySpark, and Python skills
Banking domain knowledge, specifically Credit & Lending
Working knowledge or experience with Hadoop, Hive, and Spark
Hands-on experience with HQL or SQL (Spark SQL) for data exploration
Experience with data mapping across multiple datasets and sources
Experience documenting data mapping, subsystem design, technical design, and business requirements
Exposure to logical-to-physical mapping and data processing flow consistency measurement
Experience with data model/data asset generation and identifying critical data elements
Understanding of ER diagrams and data modeling concepts
Exposure to data quality validation, data management, data cleaning, data preparation, and data schema analysis
Exposure to Agile frameworks
Graduate in Computer Science, Data Science, or a related field
3–4 years of experience in data engineering or a related field
Jupyter Notebook, Jira, Confluence, and Microsoft Excel
Exposure to at least one cloud platform (GCP, AWS, or Azure) is a plus
Knowledge of Credit Risk Frameworks such as Basel II, Basel III, IFRS 9, and stress testing is advantageous

Benefits

Comp & perks

Hybrid work arrangement
Full-time employment