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Tier-1 brand, mid-level experience, and metro location increase competition despite niche streaming skills.
Streaming data platform expertise is transferable across industries but leans technical, yielding medium cross-industry fit.
Explicit minimum experience, domain-specific streaming technologies, and core Java mandate strict filters.
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Design, develop, and maintain a scalable data platform for streaming and batch processing using Java and technologies like Apache Flink, Kafka, and Trino.
Advise data engineers to optimize real-time and batch data applications meeting low-latency requirements.
Collaborate across teams to deliver event-driven architectures and ensure code quality throughout the SDLC using Agile methodologies.
Bachelor’s degree in Computer Science, Engineering, or related field.
Minimum 5 years of experience developing and deploying production-ready Java applications in a data engineering context.
Strong experience with Java 11+, SQL, database APIs, and distributed stream processing frameworks (Apache Flink, Spark Streaming, Kafka Streams).
Experience with event-driven architectures and real-time data processing.
Experienced in building and optimizing low-latency, large-scale streaming and batch data pipelines with Java expertise.
Comfortable advising and collaborating with engineering teams to implement scalable event-driven solutions.
Experience working in Agile environments and knowledge of containerization and cloud platforms is a plus but not mandatory.