Data Team Lead

Our partner is a bank in Ethiopia investing in the development of its data platform to strengthen analytics, reporting, and data-driven decision-making across the business. Role purpose: Lead the design and implementation of the bank’s target data platform, establish data engineering standards and governance, and build an effective data engineering team. This is a fully hands-on leadership role combining architectural ownership with direct technical contribution. Responsibilities Design and evolve the target lakehouse/DWH architecture, covering data ingestion, processing, storage, and serving Lead the development of 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 and other operational sources Develop the Data Lake, ClickHouse analytical data marts, and serving layers for BI, ML, and AI applications Design real-time data streams and marts to support fraud prevention and risk management Establish data contracts, schema registry, schema compatibility rules, and data source onboarding standards Implement data governance practices, including lineage, data cataloging, data quality monitoring, and PII classification Define and maintain SLAs/SLOs for data freshness, completeness, and correctness Improve pipeline performance, optimize storage and compute costs, and establish CI/CD, automated testing, and observability Develop data architectures supporting analytics, feature stores, ML/GenAI, and RAG use cases Lead and develop the data engineering team, including team planning, recruitment, performance reviews, and technical mentoring Collaborate with Analytics, ML, Product, Engineering, Security, and banking domain teams to align the platform with business needs Requirements At least 3 years of hands-on Data Engineering experience, combined with a proven track record of technical leadership Experience designing and building production data platforms, from ingestion to analytical data marts and serving layers Strong SQL and Python skills Hands-on experience with Apache Airflow, Spark, and Kafka Experience with batch and streaming processing, CDC, and handling late or duplicate events Understanding of lakehouse/DWH architectures, data modeling, and data platform layers Practical experience with data contracts, schema evolution, and schema registries Experience implementing CI/CD, automated testing, and monitoring for data pipelines Understanding of data governance, lineage, data catalogs, data quality, and PII protection Experience managing a team or demonstrating sustained technical leadership across multiple data projects English proficiency at Intermediate level or above Nice to have Experience with ClickHouse MergeTree and optimization of high-load analytical queries Practical experience with MLOps, feature stores, and collaboration with ML engineers Experience building production GenAI/RAG pipelines Experience developing real-time data streams for fraud prevention, risk management, or other critical business decisions