Senior Data Backend Engineer

• Design, develop, and optimize scalable backend services and data pipelines for processing high-volume, complex datasets.
• Build efficient data processing frameworks and workflows to support analytics, machine learning, and AI-driven applications.
• Develop and maintain distributed systems that ensure reliability, scalability, and high performance.
• Create solutions that streamline data ingestion, transformation, storage, and retrieval processes.
• Partner with cross-functional teams to enable seamless deployment and operationalization of machine learning models.
• Improve platform efficiency through performance tuning, automation, and best engineering practices.
• Contribute to the architecture and evolution of cloud-native data platforms and services.

Are you passionate about building robust backend platforms that transform large-scale data into meaningful insights and power next-generation AI solutions? We are seeking an experienced Senior Data Backend Engineer to develop and enhance high-performance data processing systems, scalable services, and intelligent data platforms that support advanced analytics and machine learning initiatives., * 6-8+ years of experience in software engineering, backend development, data engineering, or a related technical field.

• Strong programming expertise in Python and/or Golang.
• Hands-on experience with distributed computing and large-scale data processing technologies.
• Proficiency in SQL and data-intensive application development.
• Experience working with technologies such as Kubernetes, Helm, Hive, and Parquet.
• Familiarity with vector databases and modern data storage architectures.
• Strong understanding of microservices architecture, scalable system design, and cloud-based environments.
• Excellent analytical, troubleshooting, and problem-solving skills.

Preferred Qualifications

• Experience supporting machine learning and AI workflows in production environments.
• Knowledge of modern big data ecosystems and data platform optimization techniques.
• Exposure to highly scalable data infrastructure, automation frameworks, and performance-focused engineering practices.