Lead Data Engineer at Equity Bank Kenya

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.

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.