Backend Java Engineer (Web3/Blockchain Background Preferred)

Requirements

• Design, develop, and operate backend systems that power blockchain analytics, attribution, and risk intelligence products
• Build scalable, high-throughput services to process and serve large volumes of on-chain data, including transactions, addresses, entities, and relationships
• Implement backend components supporting address clustering, entity resolution, and transaction graph analysis
• Collaborate closely with data scientists, analysts, and product teams to productionize analytical models and heuristics
• Design APIs and internal services that expose blockchain intelligence to downstream applications and customers
• Improve system reliability, performance, and maintainability through thoughtful architecture and continuous optimization
• Write clear technical documentation and contribute to engineering best practices

Responsibilities

• Bachelor’s degree or above in Computer Science, Engineering, or a related field
• 3+ years of backend development experience using Java
• Strong experience with Spring / Spring Boot / Spring Cloud in production environments
• Solid understanding of backend infrastructure and data stores, including relational databases (MySQL, PostgreSQL), caching systems (Redis), and messaging systems (Kafka)
• Strong understanding of blockchain fundamentals, such as public blockchain data structures (blocks, transactions, addresses), UTXO vs account-based models, and on-chain data ingestion and normalization
• Ability to work effectively in a data-heavy, problem-driven environment
• Strong English communication skills, both written and spoken
• Working proficiency in Chinese to collaborate with regional stakeholders

Nice to Have

• Experience at blockchain analytics, crypto compliance, or fintech companies
• Hands-on experience with graph-based systems, including address clustering, wallet attribution, entity resolution, and transaction graph traversal
• Familiarity with clustering algorithms (heuristic, rule-based, or ML-assisted) applied to real-world datasets
• Experience with big data frameworks such as Spark or Flink
• Exposure to AML, sanctions screening, risk scoring, or compliance-related systems
• Experience building or operating distributed systems at scale
• Familiarity with Docker, Kubernetes, and cloud-native architectures