Lead Data Engineer Job Equity Bank

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The Role Purpose

Understand multiple banking databases and table structures; create a complete data map and documentation. Design and implement efficient SQL queries and Python scripts to extract, clean, and transform data for analytics, ML and AI. Build automated ETL/ELT pipelines with data quality checks and monitoring. Deliver curated, analysis-ready datasets and maintain data lineage and governance compliance.

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The KEY 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. 

Qualifications

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. 

Technical Competencies: 

• Expertise in SQL (complex joins, optimization) and Python for data wrangling and automation. 
• Strong understanding of data modeling, ETL/ELT processes, and pipeline orchestration tools. 
• Ability to troubleshoot performance issues and ensure data quality and integrity. 

Leadership/Soft Skills: 

• Excellent problem-solving and critical thinking abilities, with a structured approach to troubleshooting and solution design. 
• Strong verbal and written communication skills, with a focus on clear documentation, code readability, and stakeholder engagement. 
• Demonstrated ownership and accountability, consistently delivering high-quality outputs and taking initiative to resolve blockers. 
• Flexible and adaptable in fast-paced, dynamic environments, able to shift priorities and handle ambiguity effectively. 
• Proven ability to collaborate within cross-functional teams, fostering a positive team culture and sharing knowledge across disciplines. 

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How to Apply

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