Senior Data Architect

Role Overview

As Senior Data Architect, you will shape how M-PESA Africa designs, connects, protects, and uses data across its markets. You will set practical architecture standards for high-volume payment and operational systems as well as analytics and machine learning, working with engineering, product, compliance, and senior stakeholders to make the data landscape scalable, traceable, and fit for evolving business and regulatory needs.

Key Responsibilities

• Set and maintain enterprise data architecture across transactional, operational, analytical, and AI/ML domains in a multi-market environment.
• Define data models, data flows, and integration patterns for both batch and real-time processing, covering OLTP and OLAP workloads.
• Guide platform architecture for payment processing, API analytics, fraud and AML monitoring, and regulatory reporting.
• Develop governance standards for data quality, classification, lineage, cataloguing, and master data management, working with market teams to address local rules and cross-border data requirements.
• Design integration between core M-PESA systems, external platforms, and analytics services, including API-first and event-driven exchanges.
• Govern streaming and change data capture pipelines, including deployments using technologies such as Kafka and Oracle GoldenGate.
• Establish data foundations for production AI/ML, including feature management, dependable pipelines, model-serving patterns, and assessment of vector and LLM-oriented infrastructure.
• Present architecture recommendations and decision records to review forums and senior technology leaders; mentor architects and engineers and contribute to vendor assessments.

Requirements & Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Electrical or Computer Engineering, or a related discipline.
• Experience in data architecture, data engineering, or a closely related field, including at least 3 years in a senior or lead role.
• Demonstrated success designing enterprise-scale data platforms in financial services, fintech, or telecommunications.
• Strong command of conceptual, logical, and physical data modelling for operational and analytical systems.
• Hands-on experience with at least one major cloud platform—AWS, Google Cloud, or Azure—and modern data technologies such as Spark, Databricks, BigQuery, or Redshift.
• Practical knowledge of ETL/ELT, change data capture, event streaming, and API-based data integration.
• Experience with Kafka or comparable streaming technology in high-throughput transactional environments.
• Working knowledge of enterprise data governance methods and tools, such as DAMA-DMBOK, Apache Atlas, Collibra, or equivalent.