Senior Data Architect

M-Pesa Africa is Africa's largest fintech, serving over 60 million customers and 5 million businesses across 8 countries with daily transactions exceeding $1.2 billion. They offer the M-PESA Super App, a digital financial ecosystem providing money transfer, payments, credit, savings, and wealth growth solutions. Their purpose is to transform lives by delivering innovative solutions that meet customer and community needs.

This senior individual contributor role at M-Pesa Africa requires a Senior Data Architect to lead the design, governance, and evolution of the company's enterprise data landscape across its multi-market operations. The successful candidate will be responsible for defining architectural standards and strategic direction for how data flows, is governed, and generates value across M-Pesa’s platforms, encompassing mobile money, payments infrastructure, AI, and analytics. The position demands a visionary leader who can mentor others and translate complex data challenges into scalable architectural solutions, with broad influence across engineering, product, compliance, and group-level stakeholders.

1. Manage the enterprise data architecture for M-Pesa Africa's multi-market environment, covering transactional, operational, analytical, and AI/ML data domains.
2. Establish and enforce data modeling standards, data flow patterns, and integration architecture for both real-time and batch processing pipelines.
3. Lead the architectural design for data platforms that support payment rails, API analytics, fraud and AML detection, and regulatory reporting.
4. Drive the adoption of modern data architectures like data mesh, data lakehouse, and event-driven patterns, aligning with M-Pesa’s cloud and hybrid infrastructure strategy.
5. Develop and champion enterprise-wide data governance frameworks, data quality standards, and master data management (MDM) policies across all six markets.
6. Define standards for data classification, lineage, and cataloguing, ensuring traceability from source systems to consumption layers.
7. Collaborate with Compliance, Legal, and Market teams to ensure data architectures comply with local regulatory obligations and cross-border data sovereignty requirements.
8. Architect data integration patterns between M-Pesa core systems, third-party platforms, and analytics/AI layers.
9. Work with API gateway teams to define event-driven, API-first data exchange patterns consistent with the integration layer strategy.
10. Design and govern streaming and Change Data Capture (CDC) pipelines using technologies such as Apache Kafka and Oracle GoldenGate across market deployments.
11. Design data architectures that support AI/ML use cases, including transaction monitoring, watchlist screening, customer intelligence, and predictive analytics.
12. Define feature store design, data pipeline standards, and model serving infrastructure patterns for production ML workflows.
13. Partner with the AI/ML team to evaluate and integrate vector databases, embedding pipelines, and LLM-ready data infrastructure.
14. Represent data architecture in Architecture Review Boards, design forums, and group-level governance bodies.
15. Mentor and coach mid-level data engineers and architects across markets to build data architecture capabilities within the team.
16. Create executive-level architecture artifacts, including C4/Mermaid diagrams, Architecture Decision Records (ADRs), and data strategy presentations for CTO/CIO audiences.
17. Lead vendor evaluations for data platform tools, cloud data services, and governance technologies.

1. Hold a Bachelor’s degree in Computer Science, Information Systems, Electrical/Computer Engineering, or a related field.
2. Possess extensive experience in data architecture, data engineering, or similar technical disciplines, with at least 3 years in a senior or lead role.
3. Demonstrate a proven history of designing enterprise-scale data platforms within financial services, fintech, or telecommunications sectors.
4. Exhibit deep expertise in data modeling (conceptual, logical, physical) for both OLTP and OLAP systems.
5. Have hands-on experience with cloud data platforms (AWS, GCP, or Azure) and big data technologies (e.g., Spark, Databricks, BigQuery, Redshift).
6. Be proficient in various data integration patterns, including ETL/ELT, Change Data Capture (CDC), event streaming, and API-driven data exchange.
7. Possess a strong understanding of data governance frameworks and tools (e.g., DAMA-DMBOK, Apache Atlas, Collibra, or equivalents).
8. Have experience with streaming platforms like Apache Kafka in high-throughput transactional environments.