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Open nowPosted 67 days ago

Agent架构工程师（数据方向）

dcar/referral674 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen nowAgent架构工程师（数据方向）dcar/referral · Beijing, Chinese Mainland

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The clock on this job

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12.1% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
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  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 67 days ago

The posting

我们正在构建下一代 AI 驱动的汽车服务生态。作为 Agent 架构工程师（数据方向），你将负责打造支撑 One-Conversion 核心架构的数据基座。通过实时与离线一体化数据平台，横向赋能新车、二手车、导购、外呼等全业务场景，利用全域数据资产与智能服务，实现从“流量接入”到“用户转化”的全链路数据化闭环。

岗位职责： 1、核心数据架构建设： 负责设计并实现支撑 One-Conversion 架构的实时与离线数据平台，涵盖数据采集、计算、建模全流程； 构建流批一体的数据计算引擎，支持业务分析、用户画像、车辆档案等核心数据模型的开发与迭代。解决多业务线（导购、二手车、新车等）的数据打通与协同问题，确保跨场景数据的一致性、时效性与复用性； 2、全场景数据稳定性保障： 负责实时流式任务（如 Flink）的开发、运维与优化，保障毫秒级数据处理延迟与高可用； 管理在线存储系统（如 TBase/Abase），确保数据同步导入、稳定性监控及容量规划； 建设全链路数据质量监控、Trace 追踪、自动扩缩容及降级机制，针对大模型数据管道波动设计容错与断流恢复方案； 3、全域数据资产与建模： 打通全场景业务数据，构建动态车辆档案、用户画像、行为事件等核心数据资产，形成语义化的 OneData 体系； 离线数据处理：负责大规模数据清洗、ETL、维度建模，支撑多维业务分析、归因分析与决策看板； 数据分析：基于海量二手车、新车交易行为进行深度洞察，提炼转化漏斗、意图分层等高价值标签； 数据治理：研发数据质量标准、血缘追踪及生命周期管理，提升数据资产的易用性与可信度； 4、数据服务与流转： 基于用户行为实时建模，打造高性能、低延迟的线上数据服务（统一查询接口、推荐特征、标签服务等）； 支持跨场景（导购/二手车/新车）的数据流转，驱动长周期精准触达与营销自动化，优化用户转化漏斗； 提供稳定可靠的数据 API 与事件驱动管道，支撑上层 Agent 调度与决策。

为什么加入我们？ - 场景真实：拥有海量真实的二手车、新车交易场景数据，你构建的数据平台将直接影响千万级交易决策； - 技术前沿：突破传统数仓边界，深度融合实时流处理与 Agent 智能体，解决数据驱动决策的核心难题； - 资源支持：提供充足的算力资源与跨团队协作机制，允许探索大规模分布式、流计算前沿技术。

1、技术功底： 扎实的计算机基础，丰富的大规模分布式并发系统研发与工程架构经验； 精通 Golang 或 Python，并熟悉主流大数据技术（Hadoop/Spark/Flink/Kafka 等）； 2、实时与离线数据经验： 必须具备实时数据处理（如 Flink/Storm）及流式任务运维优化经验，同时熟悉离线数据仓库建模、ETL 及数据分析方法论； 3、架构能力： 熟悉分布式架构、微服务治理、高并发处理，有大型系统工程架构设计能力，对在线存储体系（如 HBase/ClickHouse/Redis）及数据湖/数据仓库架构有深度实践； 4、AI Agent 或推荐算法经验： 了解大模型原理，有 AI Agent 开发框架（如 LangChain、AutoGPT 等）或推荐系统（召回/排序）相关开发与优化经验，能从数据侧支撑智能调度与决策； 5、产品思维： 具备优秀的数据业务意识，能从数据视角反推业务创新，不局限于“按需求开发”； 6、加分项： 拥有复杂实时流系统开发、数据平台建设、汽车行业垂直领域数据工程经验，或 Agent/推荐系统与数据管道结合实战经验者。

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