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https://worldinsiderweb.com/beautiful-images-for-good-morning/Location: Remote (Preferred EST /CST)
Duration: Contract
Must need (12+) years of Experience.
Must have Skills:
Candidate having strong experience working with Capital Markets Domain specifically in Risk, Clearing & Trading.
With Kafka Streaming experience.
OR
Candidate having 8-10yrs of experience primarily into Financial Industry as relevant but should be between 12-16yrs overall.
Strong in Kafka Streaming is a Must
Key Skills & Responsibilities:
• Hands-on development experience in Java 8/11 (Stream API, Multi-threading, Collections. Profiling) / JEE and Spring MVC / Spring Boot
• Low-latency, high throughput experience using Kafka and NoSQL databases is ideal
• Experience of writing applications for deployment on cloud environment
• Experience with containerization (e.g., Docker) and preferably, Container Management software (Kubernetes)
• Hand-on experience with Unit testing (Mockito)
• Excellent communication skills
• Financial services experience is a big plus
Thanks
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In the world of data science, raw data serves as the foundation for generating actionable insights. However, managing, processing, and transforming this data into a usable format requires specialized tools. Data engineering plays a pivotal role in the data science workflow by ensuring data availabil..
https://reshukhushi.wordpress.com/2024/12/03/top-data-engineering-tools-for-data-science-professionals/In a competitive job market, having a certification can set you apart from other candidates. The Confluent Kafka Certification is recognized by industry leaders and can significantly enhance your employability. It demonstrates your commitment to professional growth and expertise in a critical technology.
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$34.00
Contact for Any Problem and more details
Email: bestpvastoregmail.com
Skype: live:.cid.9f57d80b1ad18e87
Telegram: @bestpvastore
WhatsApp: +1 (959) 400-3293
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Data engineering tools include Apache Spark, Hadoop, Airflow, Kafka, and SQL for processing; Python and Scala for scripting; AWS, Azure, and GCP for cloud storage; and tools like dbt, Snowflake, and Databricks for transformation and analytics workflows.
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