We are seeking a highly skilled .NET Backend Developer with expertise in C#, SQL Server, MongoDB, MySQL, and large-scale data processing as core skill . This role focuses on efficient data ingestion, structured data integration, and high-speed processing of large datasets while ensuring optimal memory and resource utilization. The ideal candidate should have deep experience in handling structured and unstructured data, multi-threaded processing, efficient database optimization, and real-time data synchronization to support scalable and performance-driven backend architecture . Key Focus Areas - Efficient Data Ingestion & Processing: Developing scalable pipelines to process large structured/unstructured data files . - Data Integration & Alignment: Merging datasets from multiple sources with consistency . - Database Expertise & Performance Optimization: Designing high-speed relational database structures for efficient storage and retrieval. - High-Performance API Development: Developing low-latency RESTful APIs to handle large data exchanges efficiently. - Multi-Threaded Processing & Parallel Execution: Implementing concurrent data processing techniques to optimize system performance. - Caching Strategies & Load Optimization: Utilizing in-memory caching & indexing to reduce I/O overhead. - Real-Time Data Processing & Streaming: Using message queues and data streaming for optimized data distribution. Required Skills & Technologies Backend Development: C#, .NET Core, ASP.NET Core Web API Data Processing & Integration: Efficient Data Handling, Multi-Source Data Processing Database Expertise: SQL Server MongoDB ,MySQL (Schema Optimization, Indexing, Query Optimization, Partitioning, Bulk Processing) Performance Optimization: Multi-threading, Parallel Processing, High-Throughput Computing Caching & Memory Management: Redis, Memcached, IndexedDB, Database Query Caching Real-Time Data Processing: Kafka, RabbitMQ, WebSockets, SignalR File Processing & ETL Pipelines: Efficient Data Extraction, Transformation, and Storage Pipelines Logging & Monitoring: Serilog, Application Insights, ELK Stack CI/CD & Cloud Deployments: Azure DevOps, Kubernetes, Docker Key Responsibilities 1. Data Ingestion & Processing Develop scalable data pipelines to handle high-throughput structured and unstructured data ingestion. Implement multi-threaded data processing mechanisms to optimize efficiency. Optimize memory management techniques to handle large-scale data operations. 2. Data Integration & Alignment Implement high-speed algorithms to merge and integrate datasets efficiently. Ensure data consistency and accuracy across multiple sources. Optimize data buffering & streaming techniques to prevent processing bottlenecks. 3. High-Performance API Development Design and develop high-speed APIs for efficient data retrieval and updates . Implement batch processing & streaming capabilities to manage large data payloads. Optimize API response times and query execution plans . 4. Database Expertise & Optimization (SQL Server , MongoDB ,MySql ) Design efficient database schema structures to support large-scale data transactions. Implement bulk data operations, indexing, and partitioning for high-speed retrieval . Optimize stored procedures and concurrency controls to support high-frequency transactions . Use sharding and distributed database techniques for enhanced scalability. 5. Caching & Load Balancing Deploy Redis / Memcached / IndexedDB caching to improve database query performance. Implement data pre-fetching & cache invalidation strategies for real-time accuracy. Optimize load balancing techniques for efficient request distribution. 6. Real-Time Data Synchronization & Streaming Implement event-driven architectures using message queues (Kafka, RabbitMQ, etc.) . Utilize WebSockets / SignalR for real-time data synchronization . Optimize incremental updates instead of full data reloads for better resource efficiency. Preferred Additional Experience Experience handling large-scale databases and high-throughout data environments . Expertise in distributed database architectures for large-scale structured data storage. Hands-on experience with query profiling & performance tuning tools . Apply arrow_forward_ios highly skilled .NET Backend Developer with expertise in efficient data ingestion, structured data integration, and high-speed processing of large datasets while ensuring optimal memory and resource utilization. handling structured and unstructured data, multi-threaded processing, efficient database optimization, and real-time data synchronization to support Efficient Data Ingestion & Processing: Developing scalable Data Integration & Alignment: Merging Database Expertise & Performance Optimization: Designing high-speed relational database structures for efficient storage and retrieval. High-Performance API Development: Developing low-latency RESTful APIs to handle large data exchanges efficiently. Multi-Threaded Processing & Parallel Execution: Implementing concurrent data processing techniques to optimize system performance. Caching Strategies & Load Optimization: Utilizing in-memory caching & indexing to reduce I/O overhead. Real-Time Data Processing & Streaming: Using message queues and data streaming for optimized data distribution. scalable data pipelines to handle high-throughput structured and unstructured data ingestion. multi-threaded data processing mechanisms to optimize efficiency. memory management techniques to handle large-scale data operations. high-speed algorithms to merge and integrate datasets efficiently. data consistency and accuracy across multiple sources. data buffering & streaming techniques to prevent processing bottlenecks. high-speed APIs for efficient batch processing & streaming capabilities to manage large data payloads. efficient database schema structures to support large-scale data transactions. bulk data operations, indexing, and partitioning for stored procedures and concurrency controls to support sharding and distributed database techniques for enhanced scalability. Redis / Memcached / IndexedDB caching to improve database query performance. data pre-fetching & cache invalidation strategies for real-time accuracy. load balancing techniques for efficient request distribution. event-driven architectures using WebSockets / SignalR for incremental updates instead of full data reloads for better resource efficiency. Expertise in distributed database architectures for large-scale structured data storage.