Web3 Senior Data R&D Engineer

Job Overview
Architect and deliver end-to-end financial and Web3 data infrastructure for AI applications—covering ingestion, standardization, metric processing, and data services. Take full ownership of core technical modules while optimizing data quality, stability, and team efficiency.
Key Responsibilities
Data Architecture: Design scalable time-series pipelines, data models, and API contracts for AI consumption; balance delivery speed with long-term extensibility.
Ingestion & Governance: Build reusable adapters for stocks/ETFs, macro metrics, news/social feeds, on-chain data, and DeFi/derivatives. Resolve asset mapping, schema drift, and historical backfills.
Analytics & Signals: Translate research needs into calculation logic for price anomalies, flow tracking, and market sentiment to power AI signals and backtesting.
AI Data Layer: Build low-latency, traceable data APIs tailored for AI Agents, RAG, and Tool Calling.
Reliability & Performance: Manage scheduling, concurrency, idempotent writes, and data replay mechanisms. Optimize query latency, memory, and API costs.
Engineering Standards: Own full delivery lifecycles, conduct code reviews, write technical documentation, and champion AI-assisted workflows.
Qualifications
Core Java: 3+ years backend Java experience (Java 21 / Spring Boot 3 preferred) with proven domain ownership. Deep knowledge of concurrency, JVM/GC tuning, and transactional systems.
Data & Storage: Strong SQL/PostgreSQL skills (partitioning, indexing, execution plans) and JPA/Hibernate experience. Skilled at batch writes and time-series performance optimization.
Pipeline Governance: Hands-on experience with multi-source adapters, rate limiting, cursor handling, out-of-order data, numerical precision, and lineage tracking.
Distributed Systems: Proficiency with Redis, Dubbo, Nacos, or SchedulerX; sound grasp of caching, distributed locks, retry/isolation patterns, and observability.
Domain & Analytics: Practical domain knowledge in traditional finance or Web3. Able to audit metric logic, spot point-in-time errors, and validate research data via SQL/scripts.
AI-Assisted Dev: Active user of AI tools (e.g., Claude Code, Codex) for task decomposition, refactoring, and test generation while maintaining strict quality control over generated code.
Bonus Points
Experience with market adjustments (splits/dividends), trading calendars, and look-ahead bias prevention.
Hands-on with multi-chain indexing, DeFi, address profiling, or chain reorgs.
Experience shipping RAG pipelines, AI Agents, or Model Context Protocol (MCP) integrations.
Track record of leading technical designs or mentoring engineers.

Work arrangement
No