Build Scalable Data Infrastructure: Design, develop, and optimize ETL/ELT pipelines and data warehouses (e.g., ClickHouse) to support analytics, business intelligence, and AI/ML workloads, ensuring scalability, performance, and cost-efficiency.
Ensure Data Quality and Governance: Implement proactive quality frameworks (e.g., dbt tests, Great Expectations) with automated validation, anomaly detection, and alerts to ensure 99.99% data accuracy, completeness, and timeliness. Enforce data masking and compliance with local regulations (e.g., Kenya’s Data Protection Act).
Support AI and Self-Service Data Platform: Collaborate with the Senior Data Scientist to build and optimize data pipelines that power the AI-led self-service data platform, ensuring high-quality data for generative AI, LLMs, and predictive models like Credit Scoring.
Drive AI Innovation: Proactively explore and implement AI-driven use cases in data engineering, such as automated data cleansing or real-time anomaly detection, to enhance operational efficiency and support business processes.
Data Cataloging and Lineage: Implement data cataloging tools and metadata frameworks to enable efficient data discovery, traceability, and compliance, supporting issue resolution and stakeholder needs.
Data Access Management: Implement and manage role-based access control (RBAC) for data pipelines and warehouses, ensuring appropriate permissions for stakeholders without over-provisioning. Monitor usage and streamline access to maintain security and compliance.
Monitoring and Optimization: Build and maintain dashboards (e.g., Grafana) and alerts to monitor pipeline health, uptime, and quality metrics. Optimize query performance and resource utilization for AI and analytics workloads.
Root Cause Analysis and Ad Hoc Support: Investigate data quality issues raised by Data Business Partners or business users, perform root cause analysis, and implement sustainable fixes. Address ad hoc requests, such as data extracts or one-off dashboards, to support the BI team.
Continuous Improvement: Driven by curiosity and innovation, identify and implement enhancements to data engineering processes (e.g., optimized pipelines, streamlined data extraction). Take ownership of initiatives, ensuring follow-through to deliver reliable, efficient solutions.
Qualifications
Bachelor’s degree in Data Science, Computer Science, Statistics, Engineering, or a related field.
2–5 years of professional experience in data engineering, with a proven track record in building and optimizing scalable data pipelines.
Demonstrated expertise in designing ETL/ELT workflows and data warehouses for analytics and AI applications.
Strong interpersonal and communication skills, with the ability to convey technical concepts to non-technical stakeholders and collaborate across teams.
Strategic thinker with an innovative mindset, driven by curiosity to uncover new opportunities and a strong sense of ownership to ensure high-quality, reliable data infrastructure.
Cultural adaptability to align data solutions with local business contexts in Kenya, Uganda, or Côte d'Ivoire, ensuring compliance with regional regulations.
Creative problem-solver who thrives in dynamic environments, prioritizes data accuracy, and drives process improvements.