Background

The Co-operative Bank of Kenya Limited is incorporated in Kenya under the Company Act and is also licensed to do the business of banking under the Banking Act. The Bank was initially registered under the Co-operative Societies Act at the point of founding in 1965. This status was retained up to and until June 27th 2008 when the Banks Special General Meeti...

The Role

Specifically, the successful jobholder will be required to:

• Design, develop, test and maintain batch, streaming and real-time data pipelines that integrate data from approved source systems into the Bank’s data and AI platforms.
• Build reusable, well-structured data products and feature sets that support business intelligence, analytics, AI solutions and informed decision-making.
• Implement data quality controls, reconciliation, metadata, lineage and monitoring mechanisms to ensure data accuracy, completeness, consistency and timeliness.
• Apply data security, privacy, access controls, encryption and retention requirements in line with the Data Protection Act, 2019, and the Bank’s policies and standards.
• Optimise data pipelines for performance, reliability, scalability and cost, ensuring effective recovery, auditability and operational resilience.
• Collaborate with cross-functional teams and business data owners to translate business requirements into fit-for-purpose datasets and data solutions.
• Integrate automated testing, source control and CI/CD practices into data engineering workflows, supporting deployments across development, testing and production environments.
• Monitor production pipelines, troubleshoot incidents, resolve defects and maintain technical documentation and operating procedures.
• Develop data ingestion and processing pipelines for unstructured data, including documents, text and voice, to support generative AI applications and technologies such as vector databases.

Skills, Competencies and Experience

The successful candidate will be required to have the following skills and competencies:

• A bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, Mathematics or a related field.
• At least four (4) years of relevant experience in data engineering, ETL/ELT development, database engineering or big-data implementation.
• Hands-on experience building and supporting production data pipelines and curated analytical datasets.
• Strong proficiency in SQL and Python, Scala or comparable programming languages.
• Professional certification in data engineering, cloud data platforms, databases or analytics will be an added advantage.
• Experience in banking, financial services or another regulated data environment will be an added advantage.

• Design, develop, test and maintain batch, streaming and real-time data pipelines that integrate data from approved source systems into the Bank’s data and AI platforms.
• Build reusable, well-structured data products and feature sets that support business intelligence, analytics, AI solutions and informed decision-making.
• Implement data quality controls, reconciliation, metadata, lineage and monitoring mechanisms to ensure data accuracy, completeness, consistency and timeliness.
• Apply data security, privacy, access controls, encryption and retention requirements in line with the Data Protection Act, 2019, and the Bank’s policies and standards.
• Optimise data pipelines for performance, reliability, scalability and cost, ensuring effective recovery, auditability and operational resilience.
• Collaborate with cross-functional teams and business data owners to translate business requirements into fit-for-purpose datasets and data solutions.
• Integrate automated testing, source control and CI/CD practices into data engineering workflows, supporting deployments across development, testing and production environments.
• Monitor production pipelines, troubleshoot incidents, resolve defects and maintain technical documentation and operating procedures.
• Develop data ingestion and processing pipelines for unstructured data, including documents, text and voice, to support generative AI applications and technologies such as vector databases.

• SQL
• Python
• Scala
• ETL/ELT development
• database engineering
• big-data implementation
• data engineering
• data pipelines
• analytical datasets
• automated testing
• source control
• CI/CD practices
• unstructured data processing
• vector databases

• A bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, Mathematics or a related field.
• At least four (4) years of relevant experience in data engineering, ETL/ELT development, database engineering or big-data implementation.
• Hands-on experience building and supporting production data pipelines and curated analytical datasets.
• Strong proficiency in SQL and Python, Scala or comparable programming languages.
• Professional certification in data engineering, cloud data platforms, databases or analytics will be an added advantage.
• Experience in banking, financial services or another regulated data environment will be an added advantage.