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Responsibilities:
Design, develop, and maintain robust and scalable data pipelines using Java and related technologies (e.g., Apache Spark, Apache Flink, Kafka).
Build and optimize real-time and batch data processing applications to support low-latency requirements.
Implement data integration solutions between various data sources and targets, including databases, APIs, and streaming platforms.
Work with MPP platforms like Trino (Presto) and Snowflake to process and analyze large datasets.
Contribute to the design and development of event-driven architectures.
Write clean, well-documented, and testable code.
Collaborate effectively with other engineers, product managers, and stakeholders throughout the software development lifecycle (SDLC), adhering to Agile methodologies.
Stay up-to-date with the latest trends and technologies in the data engineering space.
Qualifications:
Bachelor’s degree in Computer Science, Engineering, or a related field.
Minimum 5 years of experience developing and deploying production-ready Java applications in a data engineering context.
Strong experience with core Java (version 11 or higher), SQL, and database APIs.
Proven experience working with distributed stream processing frameworks like Apache Flink, Spark Streaming, or Kafka Streams.
Experience with event-driven architectures and real-time data processing.
Solid understanding of OOP concepts, multithreading, and thread pools.
Familiarity with containerization technologies like Docker and deployment platforms like Openshift, ECS, or Kubernetes.
Excellent communication and collaboration skills.
Nice to Have:
Master’s degree in a relevant field.
Contributions to open-source projects.
Experience working in a cloud environment (AWS, GCP).
Responsibilities:
Qualifications:
Education:
This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.
Time Type:
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