Mid Data Engineer

Role Overview We are looking for a versatile and driven Data Engineer who can work across Data Engineering, Analytics, and Data Science. The ideal candidate will be comfortable building scalable data solutions,analyzing business information, and applying machine learning to address real-world fintech use cases. Qualifications & Experience ● Bachelor’s degree in Computer Engineering, Computer Science, Data Science, Statistics, Mathematics, or a related discipline. ● 3–5 years of relevant experience in Data Engineering, Data Analytics, Data Science, or Big Data environments. ● Graduates from TU, KU, or other recognized universities are encouraged to apply. Key Responsibilities ● Develop, manage, and optimize scalable data pipelines and ETL/ELT processes. ● Work with Data Warehouse and Data Lakehouse architectures. ● Manage large structured and unstructured datasets while maintaining data quality, security, and governance. ● Conduct data analysis to identify customer behavior, transaction patterns, and business trends. ● Develop dashboards, KPIs, BI reports, and management-level insights. ● Translate data findings into actionable recommendations for business decisions. ● Build and deploy predictive and machine learning models for fintech applications. ● Develop solutions for customer segmentation, recommendations, and personalization. ● Monitor model performance and continuously improve business outcomes. Required Skills ● Strong proficiency in Python, including Pandas, NumPy, and Scikit-learn. ● Working knowledge of Scala and Java. ● Advanced SQL and database management skills. ● Hands-on experience with Apache Spark/PySpark. ● Knowledge of data visualization, data modeling, and ETL/ELT processes. ● Understanding of machine learning concepts and model deployment. Preferred Big Data Technologies ● Hadoop / HDFS ● Apache Spark ● Apache Iceberg ● Apache Airflow ● Apache Kafka ● Data Lakehouse technologies