Senior Software Engineer - Data Integration & JVM Ecosystem
New
USFull-TimeSenior
Salary125,600 - 185,500 USD per year
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Job Details
- Experience
- 6+ years
- Required Skills
- SQLJavaJVMSparkData modelingDistributed Systems
Requirements
- 6+ years of software engineering experience focused on scalable, data-intensive applications and distributed systems.
- Strong expertise in Java and the JVM ecosystem, including performance profiling, garbage collection optimization, and concurrent programming patterns.
- Hands-on experience with the internals of at least one major data processing framework such as Apache Spark, Apache Flink, Kafka Connect, or Apache Beam.
- Proven background building or extending connectors, sinks, sources, or SDKs for large-scale data integration environments.
- Solid understanding of SQL, data modeling, query optimization, and analytical or OLAP database concepts.
- Familiarity with networking fundamentals including TCP/IP, HTTP, JDBC, and techniques for efficient data transfer.
- Excellent communication and collaboration skills, with the ability to work effectively across distributed engineering teams.
- Passion for open-source development and developer-focused tooling.
- Additional experience with Python, data engineering ecosystems, or modern analytics platforms is considered a strong advantage.
Responsibilities
- Design, develop, and maintain high-performance integrations and connectors for modern data processing frameworks such as Apache Spark, Apache Flink, Kafka Connect, and Apache Beam.
- Own the full lifecycle of JVM-based data integration tooling, including database drivers, SDKs, connectors, and data pipeline components.
- Optimize large-scale data ingestion and processing systems to ensure scalability, reliability, and low-latency performance across distributed environments.
- Collaborate with cross-functional engineering teams, enterprise users, and open-source communities to improve developer experience and integration capabilities.
- Contribute to performance tuning initiatives involving JVM internals, memory management, concurrent programming, and network throughput optimization.
- Support the continuous evolution of data engineering infrastructure through code reviews, technical design discussions, and best engineering practices.
- Help shape and improve integrations with analytics, visualization, and observability platforms used across modern data ecosystems.
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