Data Engineer大数据工程师

Data Engineer大数据工程师 HangzhouExperiencedFull-timeResponsibilitiesResponsibilities 1.Scale Lakehouse & Data Pipelines Iterate on the Iceberg lakehouse architecture; build and maintain multi-source batch and streaming pipelines to guarantee stability, data consistency and timeliness. 2.Data Modeling & Metric System Build layered data models and standardized unified metrics (user, transaction, traffic, commercialization) via dbt to support self-service analytics and cross-functional business collaboration. 3.Query & Cost Optimization Optimize performance and cost on computing engines including Spark, Glue and ClickHouse to boost dashboard and ad-hoc analytics experience. 4.Data Engineering, Observability & AI-Powered Exploration Improve CI workflows, documentation, data quality and alerting systems; iteratively build AI-native data engineering capabilities such as automated data quality diagnosis, anomaly root-cause analysis, and data asset search & Q&A. 你将负责 1. 湖仓与管道规模化：演进 Iceberg 湖仓；建设/维护多源批流链路，保障稳定性/一致性/时效性； 2. 建模与指标体系：用 dbt 沉淀分层模型与统一口径指标（用户/交易/流量/商业化），支撑自助分析与业务协作； 3. 查询与成本优化：在 Spark / Glue / ClickHouse等上做性能与成本优化，提升看板与分析体验； 4. 工程化与可观测 + 智能探索：完善 CI、文档、质量与告警体系；增量推进“AI 为内核”的数据工程能力（质量诊断、异常定位、资产检索/问答等）。QualificationsRequirements 1.Over 1 year of data engineering experience Solid SQL proficiency; capable of abstracting business logic into data models; familiar with layered modeling, consistent metric standards, incremental loads, backfilling and idempotency. 2.Strong coding capability Able to write production-grade code with Python/Java/Go or equivalent; strong engineering awareness covering logging, exception handling and basic unit testing. 3.Hands-on lakehouse & cloud experience Familiar with mainstream databases and data lake architectures & features; practical experience with S3-compatible storage paired with distributed compute engines. 4.Strong ownership mindset Able to break down and estimate tasks reasonably; quickly troubleshoot and resolve issues, then conduct post-mortem reviews to accumulate experience; align metric standards with cross-functional teams and drive deliverables to completion. Nice to Have 1.Practical implementation and optimization experience with Iceberg, Spark, Athena, Trino, Glue, DuckDB or ClickHouse. 2.In-depth dbt expertise (tests, documentation, CI/CD, macros) or ClickHouse/OLAP performance tuning experience. 3.Experience building event tracking pipelines and growth analytics (e.g. PostHog, funnel analysis, retention, attribution, A/B experiment metrics). 4.Familiarity with third-party data schemas such as Stripe and advertising platforms; or experience prototyping data Copilot / intelligent data tools. What You’ll Get 1.Hands-on experience with next-gen data architecture Work with a modern data stack built on Iceberg + Glue + dbt + Dagster to build more stable, faster and cost-efficient data pipelines. 2.AI-first working methodology Leverage state-of-the-art AI tools to streamline intelligent data quality checks, troubleshooting, metric standardization and data asset management with higher efficiency. 3.High impact & fast feedback for accelerated growth High ownership scope with rapid outcome feedback and strong sense of achievement. Collaborative engineering culture that encourages standardized SOPs and shared best practices. 我们希望你具备 1. 1 年+ 数据工程经验：SQL 扎实，能把业务抽象为数据模型；理解分层建模、口径一致性、增量/回填/幂等等； 2. 代码能力扎实：生产级别的代码能力（Python/Java/Go等），工程思维强（日志、异常处理、基础测试意识）； 3. 湖仓/云上实践：熟悉各种常见数据库与数据湖架构、特性；有 S3 类存储 + 计算引擎经验； 4. Ownership：对任务进行有效拆解与评估，遇到问题能快速定位修复，并复盘沉淀；跨团队对齐口径并推进交付。 加分项（Nice to have） 1. Iceberg / Spark / Athena / Trino / Glue / DuckDB / Clickhouse 的实际落地与优化经验； 2.深度 dbt（tests/docs/CI/CD/宏）或 ClickHouse/OLAP 优化经验； 3.埋点链路构建经验与增长分析经验（例如使用过PostHog；漏斗/留存/归因/实验口径）； 4.熟悉 Stripe/广告渠道等第三方数据模型，或做过“数据 Copilot/智能工具”原型 你将获得（What you’ll get） 1.新一代数据架构实战：Iceberg + glue + dbt + Dagster 的现代数据栈，做出更稳、更快、更省的链路； 2.AI 驱动的方法论：用最先进的AI工具，把质量、排障、口径与资产沉淀做得更智能、更高效； 3.高影响力+即时反馈 = 高速成长：权限大、成果反馈快、成就感强；技术氛围好，鼓励沉淀 SOP 与最佳实践。Apply