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.
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 (Personally Identifiable Information) 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.
Requirements
Professional Experience
- 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.
- Experience in Banking, Fintech, or Digital Lending environments is highly desirable.
Education and Certifications
- 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 and 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.
How to Apply
Interested and qualified? Go to Equity Bank Kenya on equitybank.taleo.net to apply.
