Data Team Lead - Data Platform

About the role

A bank in Ethiopia is investing in the development of its data platform to strengthen analytics, reporting and data-driven decision-making. The Data Team Lead leads the design and implementation of the target data platform, sets data engineering standards and governance, and builds an effective data engineering team. It is a fully hands-on leadership role combining architectural ownership with direct technical contribution.

Key responsibilities

• Design and evolve the target lakehouse/DWH architecture, covering ingestion, processing, storage and serving.
• Lead batch and streaming data pipelines using Kafka, Airflow, dbt and Spark.
• Build streaming-first integrations with banking systems and implement change data capture (CDC) from the core banking system.
• Develop the Data Lake, ClickHouse analytical data marts and serving layers for BI, ML and AI applications.
• Design real-time data streams and marts for fraud prevention and risk management.
• Establish data contracts, schema registry, schema compatibility rules and data source onboarding standards.
• Implement data governance: lineage, data cataloging, data quality monitoring and PII classification.
• Define and maintain SLAs/SLOs for data freshness, completeness and correctness.
• Optimize pipeline performance, storage and compute costs, and set up CI/CD, automated testing and observability.
• Support analytics, feature stores, ML/GenAI and RAG use cases.
• Lead and develop the data engineering team, including planning, recruitment, performance reviews and mentoring.
• Collaborate with Analytics, ML, Product, Engineering, Security and banking domain teams.

Required profile

• At least 3 years of hands-on data engineering experience, with a proven track record of technical leadership.
• Experience designing and building production data platforms, from ingestion to analytical data marts and serving layers.
• Team management or sustained technical leadership across multiple data projects.
• Intermediate English or above.

Required skills

• Strong SQL and Python.
• Apache Airflow, Spark and Kafka; batch and streaming processing, CDC, and handling late or duplicate events.
• Lakehouse/DWH architectures, data modeling and data platform layers.
• Data contracts, schema evolution, schema registries, CI/CD, automated testing and monitoring.
• Data governance, lineage, data catalogs, data quality and PII protection.
• Nice to have: ClickHouse MergeTree, MLOps, feature stores, GenAI/RAG pipelines and real-time streams for fraud and risk.