Data Engineer

Role overview Build and operate the data foundations behind digital banking analytics, reporting, and machine learning. The work spans data pipelines, storage, processing, and governance, with a focus on reliable, high-quality handling of sensitive financial information.

Responsibilities - Design and maintain scalable ETL pipelines and batch or real-time processing systems. - Develop and optimize data warehouses, data lakes, storage, and query performance. - Create data validation and quality frameworks, and monitor pipelines to resolve failures. - Apply security, privacy, governance, and regulatory controls to data workflows. - Partner with analysts and data scientists on data needs, reporting, and machine learning inputs. - Document data architecture, processes, and cataloging practices.

Requirements - At least three years in data engineering or a related role. - Strong programming skills in Python, Java, or Scala, plus SQL and database experience. - Experience with pipeline orchestration tools such as Airflow or Luigi. - Familiarity with big data technologies such as Spark or Hadoop and data warehouse concepts. - Understanding of data modeling, schema design, and cloud data platforms. - Strong debugging, problem-solving, communication, and collaboration skills.

Nice to have Experience in banking or fintech; streaming technologies such as Kafka or Kinesis; data lake architectures; data governance and compliance; BI tools such as Tableau, Power BI, or Looker; or cloud certifications. Understanding machine learning data needs is also useful.

Benefits and work setup Full-time role with remote or hybrid work options in Dakar, Senegal. The role description highlights professional development and work connected to ethical finance.