We are seeking an experienced Data Engineer to develop a robust real-time data collection and analysis system for options trading strategies. The ideal candidate will have a strong background in Apache Kafka and distributed systems, with specific experience in handling high-frequency financial data. This project involves integrating multiple data streams, processing real-time bid/ask quotes and trade executions, and building scalable pipelines to analyze the impact of market conditions on trading activities.

Responsibilities:

• Design, implement, and manage a scalable Apache Kafka cluster for real-time data ingestion and processing.
• Develop Kafka producers to capture live bid/ask prices and trade execution data from multiple sources.
• Create Kafka consumers and stream processing applications to analyze how bid/ask changes influence transactions.
• Integrate Kafka streams with external storage solutions (e.g., SQL/NoSQL databases, data lakes) for long-term storage and backtesting.
• Optimize data pipelines for high throughput and low latency to support high-frequency trading environments.
• Ensure synchronization and alignment of bid/ask data and trade executions for accurate analysis.
• Collaborate with stakeholders to understand trading strategies and data requirements.
• Implement monitoring and logging solutions to track system performance and ensure data integrity.
• Provide documentation and training for ongoing maintenance and system enhancements.

Requirements:

• Proven experience in setting up and managing Apache Kafka clusters in production environments.
• Proficiency in programming languages such as Java (essential for Kafka), Python, or Scala.
• Strong knowledge of real-time data processing and stream processing frameworks (e.g., Kafka Streams, Apache Flink, Apache Spark).
• Familiarity with SQL and NoSQL databases for storing and querying processed data.
• Solid understanding of distributed systems architecture, including data partitioning, replication, and fault tolerance.
• Experience in financial markets, particularly options trading and market data structures, is highly desirable.
• Ability to optimize systems for low latency and high throughput in high-frequency trading scenarios.
• Excellent problem-solving skills and the ability to troubleshoot complex issues in distributed systems.
• Strong communication skills, with the ability to explain technical concepts to non-technical stakeholders.
• Degree in Computer Science, Engineering, or a related field (preferred).

Preferred Skills:

• Experience with cloud platforms (AWS, Google Cloud, Azure) for deploying and scaling Kafka clusters and related infrastructure.
• Knowledge of Docker and Kubernetes for containerization and orchestration of services.
• Understanding of quantitative analysis and backtesting methodologies for financial data.
• Prior work on projects involving real-time data analysis and high-frequency trading.

Project Scope:

• Initial setup and configuration of Kafka cluster and data pipelines.
• Development and integration of data ingestion and processing applications.
• Ongoing optimization and maintenance of the system for performance and scalability.
• Potential for long-term collaboration on additional features and enhancements.

How to Apply:

Please provide:

• A brief overview of your experience with Kafka and real-time data systems.
• Examples of previous projects or case studies relevant to financial data processing.
• Your approach to handling real-time bid/ask and trade data for analysis.
• Your availability and estimated timeline for project milestones.

تغییر taskscheduler (زمان بند) اصلی (fifo-fair) موتور پردازش جریانی Apache Spark و تعریف الگوریتم جدید (PSO-Genetic-Firework) , جایگزینی به عنوان tasckscheduler اصلی

آپاچی اسپارک : یک فریم ورک محاسبات توزیع شده اپن سورس است . اسپارک با زبان برنامه نویسی اسکالا نوشته شده است. و در محیط ماشین مجازی jvm اجرا می شود.