Data Engineer — Python, Spark & dbt

About the role

We are building East Africa's most comprehensive financial data platform and we need a Data Engineer who is passionate about turning raw, messy data into reliable, queryable truth. You will design and maintain the pipelines that power our analytics dashboards, machine learning models, credit-scoring algorithms, and regulatory reporting. In this role you will own the full data engineering lifecycle: ingestion, transformation, validation, and serving. You will build ELT pipelines that pull from our MySQL transactional database, M-Pesa callback logs, third-party credit bureau APIs, and partner data feeds. You will transform this data using dbt and store it in our AWS Redshift data warehouse in a clean, documented, well-tested dimensional model. You will work closely with analysts, data scientists, and product managers who rely on your pipelines every single day. You have strong Python skills and you are comfortable writing production-quality PySpark jobs for large-scale batch processing. You understand SQL deeply — not just SELECT statements but query optimisation, window functions, CTEs, and the performance implications of different join strategies. You have used dbt in production and you understand how to structure a dbt project with proper staging, intermediate, and mart layers, macros, and tests. You care about data quality. You build pipelines that fail loudly and visibly when something is wrong, rather than silently producing wrong numbers. You have experience with orchestration tools like Apache Airflow or Prefect, and you understand how to design DAGs that are idempotent and observable. Our stack includes Python 3.12, Apache Spark, dbt Core, Apache Airflow on AWS MWAA, AWS Redshift, S3 data lake, Metabase for BI, and Great Expectations for data quality. Experience with financial data or mobile money systems is a strong advantage. We offer a remote-first environment, strong salary, medical cover, and the opportunity to shape the data culture of a high-growth fintech from an early stage.