Lead Data Engineer
2026-02-23T08:08:27+00:00
Equity Bank
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https://equitygroupholdings.com/ke/
FULL_TIME
Nairobi
Nairobi
00100
Kenya
Banking
Computer & IT, Science & Engineering, Business Operations
2026-03-06T17:00:00+00:00
8
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.
* 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. * 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.
* Expertise in SQL (complex joins, optimization) * Python for data wrangling and automation * Strong understanding of data modeling * ETL/ELT processes * Pipeline orchestration tools * Troubleshoot performance issues * Ensure data quality and integrity * Problem-solving and critical thinking * Clear documentation * Code readability * Stakeholder engagement * Ownership and accountability * Flexibility and adaptability * Collaboration within cross-functional teams
* A bachelor’s Degree, Diploma, or professional certification in Computer Science, Software Engineering, Information Technology, or a closely related field.
JOB-699c0afb43096
Vacancy title:
Lead Data Engineer
[Type: FULL_TIME, Industry: Banking, Category: Computer & IT, Science & Engineering, Business Operations]
Jobs at:
Equity Bank
Deadline of this Job:
Friday, March 6 2026
Duty Station:
Nairobi | Nairobi
Summary
Date Posted: Monday, February 23 2026, Base Salary: Not Disclosed
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JOB DETAILS:
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.
Work Hours: 8
Experience in Months: 12
Level of Education: bachelor degree
Job application procedure
Application Link:Click Here to Apply Now
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