Data Engineer

Stanbic Bank Kenya is a leading financial institution in Kenya, providing a wide range of banking and financial services to individuals, businesses, and corporations. It was founded in 1958.

The Data Engineer will be responsible for leading the technical aspects of designing, implementing, and continuously improving the bank's enterprise data architecture, data platforms, and AI engineering solutions. This position is crucial for enabling the organization to extract significant value from its data by building scalable, secure, and well-governed data foundations, developing high-quality data products, and supporting advanced analytics, machine learning, and generative AI initiatives. The role ensures that all data solutions adhere to enterprise architecture standards, governance requirements, security controls, and strategic technology objectives, while also promoting data reliability, interoperability, and operational excellence across the bank.

1. Provide technical leadership in designing, delivering, and evolving enterprise data architecture.
2. Lead the development and continuous improvement of data platforms and AI engineering capabilities.
3. Establish scalable, secure, and governed data foundations to enable data value extraction.
4. Engineer high-quality data products to support business needs.
5. Support advanced analytics, machine learning, and generative AI use cases.
6. Ensure all data solutions align with enterprise architecture standards, governance, and security controls.
7. Promote data reliability, interoperability, and operational excellence within data solutions.

1. A First Degree in Information Technology, Mathematics, Statistics, Business Commerce, or a related discipline.
2. Relevant professional certifications in cloud data platforms, data architecture, data engineering, data science, machine learning, or AI engineering are advantageous.
3. 8-10 years of experience in building databases, data warehouses, reporting, and data integration solutions.
4. Proven experience in building and optimizing big data pipelines, architectures, and datasets.
5. Experience in creating and integrating APIs.
6. Ability to perform root cause analysis on internal and external data and processes to answer business questions and identify improvement opportunities.
7. Deep understanding of data pipelining, performance optimization, data principles, and how data fits within an organization (including customer, product, and transactional information).
8. Knowledge of integration patterns, styles, protocols, and systems theory.
9. Experience with database programming languages such as SQL, PL/SQL, SPARK, or appropriate data tooling.
10. Experience with data pipeline and workflow management tools.
11. Strong behavioral competencies in articulating information, developing expertise, interpreting data, managing tasks, producing output, and team working.
12. Technical competencies in Big Data Frameworks and Tools, Data Engineering, Data Integrity, Data Quality, IT Knowledge, and Stakeholder Management.