锦鲤求职岗位描述
岗位职责
Key Responsibilities:
1. AI-Ready Data Architecture & Strategy: Spearhead the strategic design and implementation of enterprise data pipelines dedicated to delivering high-quality, "AI-ready" datasets. Bridge the gap between raw data and AI applications by ensuring data is structured, standardized, and optimized for consumption by LLM (Large Language Model)/AI Agents via the Algorithm/Development teams.
2. Cross-Functional Synergy & Business Alignment: Act as the primary technical liaison between the data engineering team, business stakeholders, and development/algorithm teams. Deeply understand complex business scenarios and downstream LLM/AI Agent requirements, translating them into robust data architecture designs and actionable engineering roadmaps.
3. Advanced Pipeline & Unstructured Data Leadership: Oversee the development of scalable data platform and pipelines capable of ingesting, cleaning, and transforming massive volumes of multi-source heterogeneous data (especially unstructured/semi-structured pharma R&D data, literature, etc.) into formats suitable for AI Agents (data API, MCP, A2A etc.), and RAG (Retrieval-Augmented Generation) systems.
4. Data Platform & Governance for AI: Lead the construction and management of the company's pharmaceutical R&D data platform architecture design and data assets. Establish rigorous data governance frameworks, data lineage, and quality control best practices to build a trustworthy data foundation that empowers downstream AI models and advanced analytics.
5. Technical Leadership & Mentorship: Build, manage, and mentor a high-performing team of data engineers. Foster a culture of engineering excellence, continuous learning, and agile delivery, while effectively managing resource allocation to support fast-paced AI initiatives.
岗位要求
Qualifications:
1. Experience & Leadership: 5+ years of extensive experience in data engineering, data architecture, or related fields, with a proven track record in technical management and leadership.
2. AI-Data Expertise: Demonstrated experience in building data infrastructure for Machine Learning or LLM applications. Familiarity with the data preparation lifecycle for AI Agents/LLM applications (MCP, A2A, RAG) is highly required.
3. Education & Background: Master’s degree or above (Ph.D. is highly preferred) in Computer Science and Engineering, Data Science, Bio-/Chem-informatics, or highly related STEM fields.
4. Technical Mastery: Expert-level proficiency in SQL and Python. Must possess the technical depth to make high-level architectural decisions, conduct code reviews, and guide the team through complex data engineering and AI-integration bottlenecks.
5. Tools & Platforms: Deep expertise in the big data ecosystem. Extensive practical experience in architecting complex data pipelines using scheduling tools (e.g., DataWorks, DataBricks, DolphinScheduler, etc.) and modern data stack technologies.
6. Comprehensive Abilities: Exceptional strategic thinking and executive communication skills. Capable of taking full ownership of the project lifecycle and seamlessly collaborating with algorithm scientists and researchers to drive business impact.
Bonus Points:
• Industry Background: Deep domain knowledge and hands-on data processing experience in the Pharmaceutical R&D industry. Candidates with interdisciplinary academic and industrial work experience in Pharma R&D, Biology, Chemistry, or Pharmacy will be given priority consideration.
• Architecture Experience: Solid experience in enterprise data warehouse modeling in an R&D field, and a strong track record of leading large-scale data platform design and applications.