Senior Software Engineer - Java Streaming - Connectors
Senior software engineer building and maintaining high-performance JVM-based connectors, drivers, SDKs, and integrations for ClickHouse’s streaming and data engineering ecosystem. Requires 6+ years of experience with Java, distributed messaging, streaming frameworks, concurrency, and scalable data integration systems.
About the job
Responsibilities
- Build and maintain ClickHouse data ecosystem integrations across the JVM, including database drivers, SDKs, sources, sinks, and connectors.
- Own the full lifecycle of integrations with streaming and data frameworks.
- Develop high-performance tools for data-intensive workloads and optimize reliability, throughput, and developer experience.
- Collaborate with the open-source community, internal engineering teams, and enterprise users.
- Contribute to production-grade integrations for Apache Kafka, Kafka Connect, Apache Flink, Apache Beam, and related platforms.
Requirements
- 6+ years of software development experience building and delivering high-quality, data-intensive solutions.
- Experience with streaming or data integration framework internals, especially Apache Kafka, Kafka Connect, Apache Flink, or Apache Beam.
- Experience developing or extending connectors, sinks, or sources for a streaming processing framework.
- Hands-on experience with distributed messaging systems, including topic design, consumer groups, and performance tuning.
- Strong understanding of SQL, data modeling, query optimization, and OLAP databases.
- Experience building scalable data integration systems beyond simple ETL jobs.
- Strong Java and JVM expertise, including memory management, garbage collection tuning, and performance profiling.
- Experience with concurrent Java programming, including threads, executors, and reactive or asynchronous patterns.
- Understanding of JDBC, TCP/IP, HTTP, and optimizing data throughput over the network.
- Strong written and verbal communication skills.
- Passion for open-source development.
Nice-to-haves
- Contributions to open-source projects and active engagement with OSS communities.
- Familiarity with ClickHouse or similar high-performance data platforms.
- Working knowledge of Python for data engineering, including Pandas, PySpark, or Airflow.
Compensation and Benefits
- Healthcare contributions.
- Company stock options.
- Flexible time off in the United States and generous leave entitlements in other countries.
- $500 USD home office setup allowance for remote employees.
- Opportunities to attend company-wide offsites.
Skills
Java, Jvm, Apache Kafka, Kafka Connect, Apache Flink, Apache Beam, SQL, Jdbc, Python, pandas, Pyspark, Apache Airflow, Olap Databases, TCP/IP, Http
Similar jobs
Data Engineering jobs
Addepar
United States
Staff Software Engineer building scalable frameworks for high-performance financial data ingestion/distribution and AI-native products. Requires 7+ years experience with distributed systems, microservices, and data architectures; partners with product teams to drive technical direction.
Alpaca
Remote
Build and operate scalable lakehouse infrastructure, streaming and CDC pipelines, query systems, and self-serve BI capabilities. Requires 5+ years of data engineering experience, strong Kubernetes and infrastructure-as-code expertise, and hands-on experience with distributed data platforms.
NinjaTrader
Chicago, IL
Leads database architecture, performance, reliability, and developer-tooling initiatives for high-volume trading applications. Requires 8+ years of software engineering experience, expert MySQL skills, backend development expertise, and strong knowledge of distributed systems and database operations.
Flaglerhealth
Vancouver, Canada
Senior Data Engineer responsible for building and operating reliable clinical and claims data pipelines, CDC systems, quality controls, and de-identified exports. The role requires 5+ years of production pipeline experience plus strong SQL, Python, Spark, and data-governance skills.
Deepgram
United States
Own the company’s metric governance program by defining canonical metrics, enforcing them in semantic and catalog systems, improving data quality, and validating AI-agent outputs. Requires 5+ years in analytics or analytics engineering, strong SQL, production semantic-layer ownership, and experience with AI evaluation and data governance.