Amazon Web Services (AWS)The candidate should have extensive experience in designing and developing scalable data pipelines and real-time data processing solutions. As a key member of the team, the Senior Data Engineer will play a critical role in building end-to-end data workflows, supporting machine learning model deployment, and driving MLOps practices in a fast-paced, agile environment. Strong expertise in Apache Kafka, Apache Flink, AWS SageMaker, and Terraform is essential. Additional experience with infrastructure automation and CI/CD for ML models is a significant advantage.
Key Responsibilities
- Design, develop, and maintain high-performance ETL and real-time data pipelines using Apache Kafka and Apache Flink.
- Build scalable and automated MLOps pipelines for training, validation, and deployment of models using AWS SageMaker and associated services.
- Implement and manage Infrastructure as Code (IaC) using Terraform to provision and manage AWS environments.
- Collaborate with data scientists, ML engineers, and DevOps teams to streamline model deployment workflows and ensure reliable production delivery.
- Optimize data storage and retrieval strategies for large-scale structured and unstructured datasets.
- Develop data transformation logic and integrate data from various internal and external sources into data lakes and warehouses.
- Monitor, troubleshoot, and enhance performance of data systems in a cloud-native, fast-evolving production setup.
- Ensure adherence to data governance, privacy, and security standards across all data handling activities.
- Document data engineering solutions and workflows to facilitate cross-functional understanding and ongoing maintenance.
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Full Stack Developer - Averlon
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Full Stack Developer - Averlon