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\n2.定义各工站的实验结果数据结构：合成产率、纯度、保留时间、活性（IC50）、异常码的统一字段与量纲。失败孔与未命中样品的编码方式同样由本岗位定义--失败数据是模型适用域的边界信息，与成功数据同等入库，不作丢弃处理; \n3.建设数据中台的入库与回流通路：结构化入库、QC 标记与不确定度、异常谱图同等入库、版本血缘。数据按\"原始数据-结构化字段-专家标注-模型训练数据-对外交付数据\"分层归档，样品 ID、批次、纯度、方法、设备与软件版本全程可追溯; \n4.建设中台的读出面，而不只是写入面。 中台必须能被消费，否则只是数据坟场。至少覆盖四类查询：模型训练所需的数据集导出（按靶点、化学系列、数据质量分带）; 药物化学家做推进/终止判断所需的结构-活性关系表与关联数据; 对外服务客户的交付数据包生成; 单个化合物的完整履历回溯（在哪块板哪个孔合成、由哪台仪器分析、用哪个批次的酶检测）; \n5.建设跨工站的作业排程：任务优先级、设备与物料约束下的排产、冲突裁决、异常后的重排。对外服务项目优先占用产能，未被占用的设备时段自动排入标准化数据生产任务; \n6.与设备集成工程师配合，在设备模拟环境上完成调度与入库链路的端到端空跑与回归，使软件进度不被硬件到货节奏卡住; \n7.与公司总部模型团队对接数据接口：实测数据入库后触发对应预测模型再训练并产出新版本号，模型版本与训练数据快照留痕、支持回滚。\n任职要求 / Requirements\n1.本科及以上学历，计算机、软件工程、数据科学、自动化相关专业，5年以上数据平台或后端系统开发经验，有过完整的数据链路从零搭建经历（采集→入库→治理→消费）; \n2.精通 Python 与 SQL，熟悉至少一种数据存储组合（关系型 + 对象存储 + 时序或文档型），能根据数据特征做合理选型; \n3.具备数据结构设计与治理的实际经验：字段规范、量纲统一、版本演进与向后兼容、血缘追踪。这是本岗位最核心的能力，重于框架熟练度; \n4.熟悉至少一种工作流 / 调度框架（Airflow、Temporal、Prefect、Dagster 或自研），理解 DAG、重试、幂等、补数、优先级队列; \n5.具备资源约束下的排程逻辑设计能力：多任务竞争有限设备与物料时的排队、抢占与冲突裁决; \n6.理解数据质量工程：校验规则、异常检测、脏数据拦截，能在入库前设门而不是入库后补救; 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