Senior Data Engineer

Responsibilities

Routine Data Support: Leverage big data technologies to build a structured data delivery system for Compliance, Trading, and other business teams; ensure timely, accurate, and traceable data outputs, and maintain the team's existing data development workflows.

Analytics System Development: Develop analytical systems and monitoring dashboards for business teams; produce data reports to support performance attribution and data-driven operational oversight.

Data Asset Governance: Author application-layer data warehouse documentation, establish field-level data standards, and lead company-wide data asset management.

Data Agent Development: Build data agents (e.g., MCP Skills) to automate data retrieval and analysis workflows, improving team productivity.

Data Sourcing & Procurement: Build efficient, real-time data collection systems to scrape or procure the data resources the team needs — including but not limited to web scraping, procured API ingestion, and management of various procured accounts.

Requirements

Strong Python skills for data analysis and modeling (Pandas, NumPy, Scikit-learn, Matplotlib, PyTorch, TensorFlow); solid coding fundamentals.

Proficient with big data frameworks (Spark, Hadoop) and large-scale data processing; familiar with one or more OLAP engines such as Presto / Doris / StarRocks / ClickHouse.

Strong SQL skills, capable of complex queries and data modeling independently.

Experienced with AI coding tools (e.g., Codex, Claude Code, Kilo Code); able to integrate AI into daily workflows — and not just "vibe coding."

Familiar with Git and standard version control practices.

Working knowledge of Java and mainstream frameworks (e.g., Spring / Spring Boot), and of container-based deployment (Docker / Kubernetes).

Preferred

GitHub open-source contributions (self-written or AI-assisted both acceptable; any domain: scrapers, full-stack, tooling, etc.).

Experience building autonomous agents — LLM-based automation, conversational systems, MCP Servers, etc.

Practical experience in machine learning, deep learning, time-series modeling, anomaly detection, etc.