Head of Data Engineering at Kifiya Financial Technology

What You’ll Do

• Define and execute the Data Engineering strategy aligned with IDD and enterprise data goals.
• Lead and mentor high-performing teams of Data Engineers and BI Developers to deliver enterprise-grade solutions.
• Collaborate closely with the CDO, Chief of IDD, and department leads to ensure data infrastructure supports business and analytical requirements.
• Champion the vision of data as a product, enabling reusable, governed, and high-quality data assets.
• Design and oversee scalable, secure, and automated data architectures supporting both batch and real-time processing.
• Manage and optimize data pipelines, data lakes, warehouses, and streaming systems (e.g., Presto, StarRocks, Snowflake, ClickHouse).
• Partner with Platform Engineering and AI teams to integrate data infrastructure with MLOps, API services, and advanced analytics.
• Ensure data lineage, versioning, and cataloguing through integration with governance and metadata systems.
• Lead the development of data ingestion frameworks from multiple internal and external sources (APIs, databases, third-party feeds).
• Implement ETL/ELT pipelines that are efficient, reliable, and easily scalable.
• Work with the Data Governance team to ensure data quality, integrity, and standardization across all domains.
• Introduce automation and testing practices to minimize manual data handling and improve consistency.
• Oversee BI and data visualization development, ensuring timely and accurate delivery of dashboards, reports, and insights.
• Partner with business and risk stakeholders to design data models and metrics frameworks aligned to KPIs.
• Support self-service analytics capabilities through governed data layers and tools.
• Drive continuous improvement of BI performance, usability, and scalability.
• Collaborate with the CDO and Head of Data Governance to ensure compliance with data security, privacy, and protection regulations (e.g., POPIA, GDPR).
• Enforce access control, data retention, and audit standards.
• Ensure all data processes are aligned with enterprise governance and audit requirements.
• Partner with AI & Platform Engineering, Credit Risk, Data Science, and Research & Analytics teams to meet data availability and performance requirements.
• Act as a key contributor in architecture forums, technology steering committees, and innovation sessions.

What You’ll Bring

• Bachelor’s or Master’s degree in Computer Science, Information Systems, or Data Engineering.
• Minimum 8–10 years of experience in data engineering or analytics, with at least 5 years in a leadership capacity.
• Proven leadership in managing data engineering and BI teams within a complex, data-driven organization.
• Advanced knowledge of cloud data architectures (AWS, Azure, or GCP).
• Expertise in SQL, Python, Spark, Airflow, Kafka, and modern data tools such as Snowflake, Databricks, or StarRocks.
• Strong understanding of data warehousing, lakehouse architectures, and ELT/ETL patterns.
• Experience in BI and visualization tools (Power BI, Tableau, Metabase, Looker).
• Excellent communication, project management, and stakeholder engagement skills.
• Solid understanding of data governance, metadata, and data quality frameworks.
• Exposure to financial services, fintech, or regulated environments is highly advantageous.