Data Analyst, Catalog Onboarding 商品目錄分析師
发布于 26 天前普通员工/个人贡献者
值不值得投
综合评分 67
A technical data analyst role in a fast-growing e-commerce platform, focusing on data quality, pipeline automation, and operational insights with a modern tech stack.
从薪资福利、成长空间、工作节奏和岗位方向综合评估，方便横向比较。
薪资怎么样
6.5万
比数据分析中位数高
发布情况
正常在招 · 27 天前发布
公司情况
这家公司招聘很活跃
约 250 个在招 · 其中数据分析 9 个
市场机会
选择不多
台北 · 数据分析 · 9 个在招
职位详情
关于这个职位
This role as a Catalog Onboarding Data Analyst is responsible for transforming product data from diverse sources into standardized, high-quality catalog data. Using SQL and Python, you will maintain ETL pipelines, automate manual processes, and analyze operational data to identify issues, find root causes, and provide actionable insights to improve data quality and processing efficiency.
最低要求
具備 SQL 查詢開發經驗，能處理較複雜的資料查詢，並可針對查詢效能進行分析與最佳化
工作职责
開發與最佳化 SQL 查詢，協助團隊快速且準確地取得商品目錄與營運資料，並改善查詢效能
优先资格
具備 2年以上 SQL 與 Python 相關實務經驗
AI 洞察
优缺点分析
优点
- Direct impact on core business operations: Your work directly affects the quality and efficiency of product catalog onboarding, a critical process for an e-commerce business.
- Strong technical skill set: The role hones high-demand skills in SQL, Python, and data pipeline management, which are highly transferable.
- Exposure to a fast-paced, data-driven environment at a major tech company, providing valuable experience in scaling operations.
- Collaborative role: You will work closely with various teams, enhancing your communication and stakeholder management skills.
- Data complexity: You will deal with messy, multi-source data, requiring patience and strong analytical skills to resolve conflicts and inconsistencies.
- Operational pressure: Maintaining stable ETL pipelines and quickly resolving data issues can be time-sensitive and high-pressure.
- Continuous learning: The need to stay updated with evolving tools (like generative AI for data tasks) and best practices in data management.
- This position is ideal for a data enthusiast who enjoys solving complex data puzzles, is proficient in SQL and Python, and wants to apply their skills directly to improving core business processes in a dynamic e-commerce environment.
缺点 / 挑战
暂无明显挑战项
角色解读
- Technical deepening: Become a specialist in data quality, data governance, or data engineering.
- Broader impact: Transition into a Data Engineer role focusing on more complex infrastructure or a Business Intelligence Analyst role focusing on strategic analytics.
- Leadership: Potential to lead a small team or take ownership of a larger data domain within the commerce or supply chain space.
- You act as a data steward for product catalogs, transforming messy raw data from various sources into clean, standardized data for downstream systems.
- You build and maintain automated data pipelines (ETL) to ensure data flows reliably and efficiently.
- You analyze operational data to diagnose data quality issues, uncover trends, and collaborate with teams to implement solutions.
- You create dashboards and reports to monitor key metrics and provide actionable insights to improve business operations.
- Strong SQL proficiency for complex querying and performance optimization.
- Practical experience with Python for data manipulation, automation, and pipeline development.
- Solid understanding of ETL/data pipeline concepts, including error handling and monitoring.
- A keen eye for data quality, with the ability to establish validation rules and investigate anomalies.
申请策略
- Research Coupang's Taiwan business operations, especially their 'Rocket Delivery' and 'Rocket Cross-border' services, to understand the context of your role.
- Prepare specific examples from your past work that demonstrate each of the 'Basic Qualifications' points, as they are extensive and will likely be the basis for interview questions.
- Quantify your impact: Showcase specific examples where you optimized SQL queries (e.g., reduced runtime by X%), automated manual tasks (saved Y hours/week), or improved data quality (increased accuracy by Z%).
- Detail your ETL/data pipeline experience: Mention specific tools (Python libraries, cloud services) and your role in the development, maintenance, or improvement lifecycle.
- Highlight your data quality initiatives: Describe how you established validation rules, tracked data issues, or collaborated with teams to resolve them.
- Include any experience with dashboarding/reporting tools (e.g., Looker, Tableau, Power BI) and how you translated business needs into visualizations.
- Sharpen your Python skills for data engineering: Focus on libraries like Pandas, NumPy, and consider frameworks like Airflow for pipeline orchestration.
- Explore and practice with generative AI tools for data tasks (e.g., prompt engineering for data cleaning or analysis) as mentioned in the JD.
面试指南
- Use the STAR method (Situation, Task, Action, Result) to structure your answers for behavioral questions.
- For technical questions, focus on explaining your thought process: How did you break down the problem? What were the trade-offs in your approach? How did you validate your solution?
- When discussing collaboration, emphasize how you communicated technical issues to non-technical stakeholders and drove them to resolution.
- Describe a time you identified and resolved a significant data quality issue in a large dataset. What was the root cause and your solution?
- Walk us through how you would design or optimize an ETL pipeline to handle product data from multiple sources with inconsistent formats.
- Can you provide an example of a complex SQL query you wrote or optimized? What made it complex, and how did you ensure its performance?
- How have you used Python to automate a previously manual data process? What challenges did you face?
- Imagine you discover a critical data error in a dashboard that is being used by the operations team. How would you handle this situation?
职位点评
薪资福利
The compensation potential is likely aligned with the market for a technical role at a major e-commerce company, but specific salary and benefits details are not provided in the JD.
成长发展
This role offers strong technical development in core data skills (SQL, Python, ETL) and exposure to operational data at scale, which are highly valuable and transferable. The explicit mention of using generative AI adds a modern skill development aspect.
工作生活
The JD specifies an on-site location in Taipei but provides no information regarding work hours, flexibility, or overtime expectations. Work-life balance cannot be assessed from the given text.
使命价值
The role is within a high-growth e-commerce platform aiming to redefine shopping experiences, which provides a clear business context. However, the specific social impact beyond commerce efficiency is not emphasized.
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