高级数据工程师 | 远程仅限拉美地区
Senior Data Engineer | Remote LATAM Only
我们寻找经验丰富的高级数据工程师，帮助在全局ERP系统中构建强大且可扩展的数据架构。
该职位适合热衷于构建端到端数据管道、推动AI/BI解决方案，并能在快节奏、高压力环境中茁壮成长的人。
你将参与加速全球数据转型计划，确保现代企业级数据生态系统的建设。
关键职责
数据集成与管道开发
- 从各种ERP来源设计、构建和维护可扩展的ETL/ELT管道，导入中心数据湖和数据仓库。
- 使用Python、SQL、API或中间件解决方案开发结构化和半结构化数据的连接器。
数据湖与数据仓库工程
- 实现青铜层、银层和金层，用于数据摄取、清洗和整理数据集。
- 组织数据结构，以优化BI和AI系统的使用。
数据标准化与清洗
- 在产品和区域之间统一全球单位（磅、公斤、包装、线性英尺）。
- 执行来自不同系统的数据去重、增强和协调。
架构协作
- 与数据架构领导紧密合作，进行模式定义、分区策略和基础设施设计。
- 搭建和维护沙盒/测试环境，以确保安全测试。
Power BI与AI赋能
- 提供可直接使用的干净数据集，支持BI仪表板和AI/ML用例。
文档与治理
- 记录管道架构、数据转换逻辑和集成点。
- 确保遵守数据治理政策并支持元数据管理。
要求
技术要求
- 7年以上企业级数据工程经验。
- 精通：
- SQL（高级）和Python用于数据处理
- Spark或Databricks用于分布式数据工作流
- 云平台如Azure Data Lake/Blob、Synapse或类似产品
- ETL编排工具如Azure Data Factory（ADF）、Airflow或dbt
- API集成和从ERP系统（NetSuite、QuickBooks、Salesforce、RF Smart等）中获取数据
- 有以下经验：
- 主数据框架、单位转换和ERP到仓库映射
- 处理结构化和非结构化数据
- 数据建模最佳实践（星型模式、雪花模式）
软技能与工作承诺
- 英语流利（C1级别）——日常客户电话和清晰的技术文档所必需
查看英文原文
We are looking for highly experienced Senior Data Engineers to help deliver a robust and scalable data architecture across global ERP systems.
This role is ideal for someone passionate about building end-to-end pipelines, enabling AI/BI solutions, and thriving in fast-paced, high-stakes environments.
You will contribute to the acceleration of global data transformation initiatives, ensuring modern, enterprise-ready data ecosystems.
Key Responsibilities
Data Integration & Pipeline Development
- Design, build, and maintain scalable ETL/ELT pipelines from diverse ERP sources into centralized Data Lakes and Warehouses.
- Develop connectors for structured and semi-structured data using Python, SQL, APIs, or middleware solutions.
Data Lake & Warehouse Engineering
- Implement bronze, silver, and gold layers for ingestion, cleaning, and curated datasets.
- Organize data structures for optimized use in BI and AI systems.
Data Standardization & Cleansing
- Align global units of measure (lbs, kg, packaging, linear feet) across products and regions.
- Execute data deduplication, enrichment, and harmonization from disparate systems.
Architectural Collaboration
- Collaborate closely with Data Architecture leadership on schema definitions, partitioning strategies, and infrastructure design.
- Set up and maintain sandbox/staging environments for safe testing.
Power BI & AI Enablement
· Provide ready-to-use, clean datasets to support BI dashboards and AI/ML use cases.
Documentation & Governance
- Document pipeline architectures, data transformation logic, and integration points.
- Ensure adherence to data governance policies and support metadata management.
Requirements
Technical Requirements
- 7+ years of experience in enterprise-scale data engineering.
- Strong proficiency in:
- SQL (Advanced) and Python for data processing
- Spark or Databricks for distributed data workflows
- Cloud platforms such as Azure Data Lake/Blob, Synapse, or equivalents
- ETL orchestration tools like Azure Data Factory (ADF), Airflow, or dbt
- API integrations and data ingestion from ERP systems (NetSuite, QuickBooks, Salesforce, RF Smart, etc.)
- Demonstrated experience with:
- Master data frameworks, unit conversion, and ERP-to-warehouse mapping
- Handling structured and unstructured data
- Data modeling best practices (star schema, snowflake schema)
Soft Skills & Work Commitment
- Fluent English (C1 level) – required for daily client calls and clear technical documentation.
- Strong interpersonal and collaboration skills to work with cross-functional teams (BI, QA, DevOps, Business Analysts).
Nice to Have
- Experience standardizing data across global manufacturing or supply chain environments.
- Familiarity with Power BI datasets, alert triggers, and integration with messaging/email tools.
- Exposure to AI/ML pipelines, including data preparation for machine learning or anomaly detection.
Originally posted on Himalayas