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.
