Advanced Fintech Data Engineering Role
We are seeking an experienced Data Engineer with strong MLOps expertise and machine learning modeling experience in the financial domain.
Main Responsibilities:
* Data Pipeline Development: Design, develop, and maintain scalable data pipelines using Python, Airflow, and PySpark to process large volumes of financial transaction data.
* MLOps Infrastructure: Implement and optimize MLOps infrastructure on AWS to automate the full machine learning lifecycle from development to production.
* Model Deployment: Build and maintain deployment pipelines for ML models using SageMaker and other AWS services.
* Collaboration: Collaborate with data scientists and business stakeholders to implement machine learning solutions for fraud detection, risk assessment, and financial forecasting.
* Data Quality: Ensure data quality, reliability, and security across all data engineering workloads.
* Optimization: Optimize data architecture to improve performance, scalability, and cost-efficiency.
* Monitoring: Implement monitoring and alerting systems to ensure production ML models perform as expected.
Requirements:
* Experience: 3-5 years of experience in Data Engineering with a focus on MLOps in production environments.
* Skills: 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.
* Education: Bachelor's or Master's degree in Computer Science, Data Science, or related field.
* Qualifications: Working knowledge of machine learning model deployment and monitoring in production. Experience with data modeling and database systems (SQL and NoSQL). Familiarity with containerization (Docker) and CI/CD pipelines.
About the Role:
This remote role offers the opportunity to work with state-of-the-art machine learning and cloud infrastructure in a fast-paced, growth-oriented environment.
We are dedicated to building scalable, high-performance payment processing systems, fraud detection algorithms, and financial analytics platforms.