Banking + 2 more
Description
Professional Experience Levels
Lead ML Data Engineer: 5+ years of progressive experience in designing and implementing data solutions, including SQL-based extraction, Python-driven pipelines, and data architecture for analytics and machine learning.
Industry Exposure
Experience in Banking, Fintech, or Digital Lending environments is highly desirable
Must-Have –
A bachelor’s Degree, Diploma, or professional certification in Computer Science, Software Engineering, Information Technology, or a closely related field
Responsibilities
Understand and document multiple banking databases, schemas, and table relationships to create a comprehensive data map.
Write efficient SQL queries and Python scripts to extract, clean, and transform data into analysis-ready datasets.
Design and maintain automated ETL/ELT pipelines with data quality checks, monitoring, and error handling.
Collaborate with Data Scientists, Risk, and Analytics teams to deliver curated datasets for credit scoring and reporting.
Ensure compliance with data governance, security standards, and regulatory requirements for PII handling.
Optimize query performance and pipeline efficiency for large-scale, high-volume banking data.
Maintain clear documentation of data lineage, transformations, and business rules for audit readiness.
CORE ACCOUNTABILITIES AND DELIVERABLES
Build and maintain reliable data pipelines to extract, transform, and load data from multiple banking systems into curated, analysis-ready datasets.
Ensure data quality, integrity, and compliance with governance standards, including secure handling of PII and audit-ready documentation.
Optimize SQL queries and Python workflows for performance and scalability across large, complex datasets.
Deliver automated ETL processes, data marts, and clear documentation to enable analytics, credit scoring, and reporting teams.
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