Data Engineering Expert: Building Scalable Financial Systems
In our pursuit of innovation, we are seeking a highly skilled Data Engineer (MLOps) to join our dynamic team working on advanced financial technology projects. 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.
Key Responsibilities:
* Design and Development: Build robust 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: Work with data scientists and business stakeholders to implement machine learning solutions for fraud detection, risk assessment, and financial forecasting.
* Quality and Security: Ensure data quality, reliability, and security across all data engineering workloads.
* Optimization: Optimize data architecture to improve performance, scalability, and cost-efficiency.
Requirements:
* 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).
* Familiarity with containerization (Docker) and CI/CD pipelines.
* Excellent problem-solving skills and ability to work in a fast-paced fintech environment.