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Data Engineer - Insurance
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Data Engineer - Insurance

发布于 6 个月前

普通员工/个人贡献者

Jakarta, Indonesia
中级经验
全职员工
仅现场办公
本科
数据分析与科学
Big Data
ETL
SQL

AI 估算 · 25k–45k

数据工程师在东南亚科技市场属于高需求技术岗，掌握Spark、数据仓库等核心技能，薪资竞争力强，职业发展前景广阔。

职位详情

关于这个职位

这是一个位于雅加达的数据工程师职位，你将加入Grab的工程团队，负责设计和构建核心数据基础设施

主要工作包括开发可扩展的数据管道，确保数据质量与可靠性，并与数据科学家、分析师等团队协作，为业务决策提供数据支持

最低要求

至少3年以上数据工程或相关领域经验

精通 SQL，能够查询和操作大规模数据集
精通数据工程常用编程语言（例如 Python、Scala 或 Java）
具有数据管道框架和ETL工具经验（例如 Apache Spark、Airflow 或类似工具）
熟悉数据仓库概念和平台（例如 Hive、Presto、BigQuery 或 Snowflake）
具备计算机科学基础知识（算法和数据结构），并能将其应用于数据问题
理解数据建模原则和数据库设计
具有版本控制系统（如 Git）的使用经验
拥有计算机科学、信息系统或相关领域的学士学位

工作职责

设计和开发稳健、可扩展的数据管道，确保跨多个系统的无缝数据流

编写功能正确、模块化且可维护的 SQL 查询和数据转换代码（Spark）
使用常见的数据存储系统和数据库，包括数据仓库和分布式数据系统
将基本的数据建模概念应用于简单和中等复杂度的数据集
与数据科学家、分析师和软件工程师等多元化团队合作，交付数据解决方案
参与代码审查，以维护数据管道质量并分享知识
及时响应数据管道问题，并维护操作手册、仪表板和警报
通过适当的测试和监控确保数据质量和可靠性

AI 洞察

优缺点分析

优点

  • Work at a leading Southeast Asian superapp (Grab), gaining exposure to massive, real-world datasets and impactful projects.
  • Build and strengthen in-demand skills in big data technologies (Spark, Airflow, cloud data warehouses) highly valued in the market.
  • Opportunity to work in a collaborative environment with diverse teams (Data Science, Engineering), broadening your perspective.
  • The company offers competitive benefits and emphasizes employee well-being and an inclusive workplace culture.
  • Need to ensure high data quality and pipeline reliability, which can involve on-call responsibilities and troubleshooting complex issues.
  • Keeping pace with the rapidly evolving landscape of data engineering tools and best practices requires continuous learning.
  • Working in a large, fast-paced company may involve navigating complex systems and coordinating with multiple stakeholders.
  • This role is ideal for a mid-level data engineer who enjoys building robust data infrastructure, is proficient in SQL and big data tools, and wants to contribute to a data-driven product in a dynamic, regional tech leader.

缺点 / 挑战

暂无明显挑战项

角色解读

  • Technical Path: Advance to Senior/Lead Data Engineer, specializing in architecting complex data systems or moving into Data Architecture roles.
  • Domain Path: Deepen expertise in the insurance or fintech domain within Grab, becoming a subject matter expert.
  • Broader Roles: Transition into Data Science, Analytics Engineering, or Platform Engineering roles based on interest and skill development.
  • Design and build scalable data pipelines to move and process data efficiently across different systems.
  • Write and optimize SQL queries and data transformation code (e.g., using Spark) to prepare data for analysis.
  • Collaborate with Data Scientists and Analysts to understand their data needs and deliver reliable data solutions.
  • Monitor data pipelines, troubleshoot issues, and ensure overall data quality and system reliability.
  • Strong proficiency in SQL and at least one programming language like Python or Scala for data processing.
  • Hands-on experience with big data frameworks (especially Apache Spark) and workflow orchestration tools like Airflow.
  • Solid understanding of data warehousing concepts (e.g., with BigQuery, Snowflake) and data modeling principles.
  • Familiarity with version control (Git) and computer science fundamentals like algorithms and data structures.

申请策略

  • Research Grab's business, especially its insurance and financial services verticals, to understand the potential data challenges and opportunities.
  • Reflect on how your skills align with building scalable systems, as this is a core theme in the job description.
  • Quantify your experience: Mention the scale of data you've handled (e.g., TB/PB) and the impact of your pipelines (e.g., improved processing time by X%).
  • Detail specific projects where you used Spark, Airflow, or similar tools for ETL/data pipeline development. Describe your role and the outcome.
  • Highlight your SQL expertise and experience with any cloud data platforms (BigQuery, Snowflake, etc.) mentioned in the JD.
  • Include any experience collaborating with cross-functional teams like data scientists or business analysts.
  • If not already strong, practice writing complex, optimized SQL queries and review common data engineering interview problems.
  • Brush up on Spark fundamentals (RDDs, DataFrames) and understand how Airflow is used to schedule and monitor workflows.

面试指南

  • Use the STAR method (Situation, Task, Action, Result) for behavioral questions to provide structured and impactful answers.
  • For technical questions, start by explaining your thought process and assumptions before diving into the solution. Discuss trade-offs if applicable.
  • Connect your answers back to business impact. For example, how your technical solution improved decision-making or user experience.
  • Walk me through a data pipeline you designed and built. What were the challenges, and how did you ensure its reliability?
  • How would you optimize a slow-running Spark job?
  • Describe a time you had to troubleshoot a broken data pipeline. What was your process?
  • Explain a data modeling decision you made for a project. Why did you choose that schema?
  • How do you ensure data quality in your pipelines?

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