Are you a skilled professional seeking a challenging role in Data Engineering? We are looking for an experienced Data Engineer to join our team and contribute to the development of cutting-edge fintech solutions.
The ideal candidate will have strong proficiency in Python programming, data processing frameworks (PySpark), and workflow orchestration tools. Experience with AWS stack, SageMaker, Lambda, S3, and other relevant services is also highly desirable.
This role requires excellent problem-solving skills and the ability to work in a fast-paced fintech environment. If you are passionate about data engineering and want to be part of a dynamic team, we encourage you to apply for this position.
Job Requirements
* Design, develop, and maintain scalable data pipelines using Python, Airflow, and PySpark to process large volumes of financial transaction data.
* Implement and optimize MLOps infrastructure on AWS to automate the full machine learning lifecycle from development to production.
* Build and maintain deployment pipelines for ML models using SageMaker and other AWS services.
* Collaborate with data scientists and business stakeholders to implement machine learning solutions for fraud detection, risk assessment, and financial forecasting.
* Ensure data quality, reliability, and security across all data engineering workloads.
* Optimize data architecture to improve performance, scalability, and cost-efficiency.
Key Qualifications
* 3-5 years of experience in Data Engineering with a focus on MLOps in production environments.
* Strong proficiency in Python programming and data processing frameworks (PySpark).
* Experience with workflow orchestration tools, particularly Airflow.
* Hands-on experience with AWS stack, especially SageMaker, Lambda, S3, and other relevant services.
* Working knowledge of machine learning model deployment and monitoring in production.
* Experience with data modeling and database systems (SQL and NoSQL).