High-Performance Data Engineer
">
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 our payment processing systems, fraud detection algorithms, and financial analytics solutions.
About the Role:
* Design, Develop, and Maintain Scalable Data Pipelines
* Implement and Optimize MLOps Infrastructure
* Build and Maintain Deployment Pipelines for ML Models
* Collaborate with Data Scientists and Business Stakeholders
* Ensure Data Quality, Reliability, and Security
* Optimize Data Architecture
* Implement Monitoring and Alerting Systems
Key Responsibilities:
1. Data Engineering Expertise: 3-5 years of experience in Data Engineering with a focus on MLOps in production environments.
2. Programming Skills: Strong proficiency in Python programming and data processing frameworks (PySpark).
3. Workflow Orchestration Tools: Experience with workflow orchestration tools, particularly Airflow.
4. AWS Stack: Hands-on experience with AWS stack, especially SageMaker, Lambda, S3, and other relevant services.
5. Machine Learning Model Deployment: Working knowledge of machine learning model deployment and monitoring in production.
6. Data Modeling and Database Systems: Experience with data modeling and database systems (SQL and NoSQL).
7. Financial Services or Payment Processing Domain: Knowledge of the financial services or payment processing domain is highly desirable.
8. Containerization and CI/CD Pipelines: Familiarity with containerization (Docker) and CI/CD pipelines.
9. Problem-Solving Skills: Excellent problem-solving skills and ability to work in a fast-paced fintech environment.
Benefits:
The ideal candidate will have a passion for building high-performance data pipelines and ML infrastructure, as well as experience working in a fast-paced fintech environment.
Others:
This role offers the opportunity to work with state-of-the-art machine learning and cloud infrastructure in a growth-oriented environment.
],