Cloud Data Engineering Role Overview
We are seeking an experienced Data Engineer with strong MLOps expertise and machine learning modeling experience in the financial domain. In this role, you will be responsible for building robust data pipelines and ML infrastructure to support payment processing systems, fraud detection algorithms, and financial analytics solutions.
Key Responsibilities
* 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.
* Evaluate data quality, reliability, and security across all data engineering workloads.
* Orient data architecture to improve performance, scalability, and cost-efficiency.
* Implement monitoring and alerting systems to ensure production ML models perform as expected.
Requirements & Skills
* 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.
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* Working knowledge of machine learning model deployment and monitoring in production.
* Experience with data modeling and database systems (SQL and NoSQL).
* A working knowledge of financial services or payment processing domains is highly desirable.
* Familiarity with containerization (Docker) and CI/CD pipelines.
* Excellent problem-solving skills and ability to work in a fast-paced fintech environment.