出海雷达

数据分析师

Data Analyts

其他限定地区（需当地身份）
公司Aliancers
薪资未公开
工作地点United States
地域资格限定地区（需当地身份）
时区要求日间重叠约 9 小时，基本正常作息
用工类型Contractor
发布时间4 天前
数据来源Himalayas
前往 Himalayas 查看并投递 →
注意地域限制：该职位明确限定在 United States 招聘。如果你是位于中国大陆的求职者，通常需要当地工作身份才能投递，或需与雇主确认是否接受独立合同（Contractor）形式合作。

数据分析师
📍拉美地区 - 100% 远程
🤝合同制 - 美元薪资
🗣️需要高级英语能力
我们正在寻找3名数据分析师，分别服务于三个不同的团队：产品与增长；运营与财务；以及实验与数字旅程。
职位概述
寻找具备数据质量专长的多面手数据分析师。这些职位通过将测量质量嵌入到仪器、数据建模、测试和报告中，确保在产品分析、财务对账和实验测量方面提供可靠且可操作的见解。候选人将负责端到端的数据完整性、治理和跨职能协作，以推动数据驱动的决策。
主要职责

  • 数据建模与对账（跨职能）：构建并维护连接运营来源（工单、零售、库存、营销）到财务系统和产品指标的语义数据模型。设计对账流程，将运营数据集与黄金源（总账、ERP）进行比较，并通过自动化异常警报定义可接受的差异。
  • 仪器质量保证与数据质量自动化：端到端审计和验证分析仪器（网页/应用）：数据层 → 标签管理器 → GA4（或 Mixpanel/Amplitude）→ 数据仓库 → BI。验证事件触发、隐私设置和活动标签（UTMs、像素）。使用 SQL（Snowflake、BigQuery、Redshift）、Python 和 Great Expectations 等框架开发自动化数据质量测试（新鲜度、完整性、唯一性、对账），并通过 Airflow/Snowflake Tasks 进行调度并在失败时发送警报。
  • 仪表盘与报告：在 Power BI、Looker、Sigma 或 Tableau 中创建和维护反映准确指标和 QA 指标（通过/失败徽章）的仪表盘。确保指标的可追溯性和为高管及利益相关者审查做好准备。
  • 数据契约、治理与文档：为关键数据集（模式、空值性、有效性阈值）定义数据契约并实施检查以强制执行契约。记录数据模型、QA 流程、标准操作程序和运行手册；在 Jira 或 ServiceNow 中管理任务/事件日志；在敏捷环境中与产品、工程、市场、财务和运营部门合作。
  • 跨职能协作与培训：与产品、市场、财务和 IT 部门合作，规划测量、推动数据质量改进，并向业务用户传授数据治理和最佳实践。
  • 多领域数据质量保障：支持产品与增长指标的质量，收入数据等
查看英文原文

Data Analyst
📍LATAM - 100% Remote
🤝Contract - USD
🗣️Advanced English skills is required
We are looking for 3 Data Analysts, for 3 different teams: Product & Growth; Operations & Finance; and Experimentation & Digital Journey.
Role Summary
Seek versatile Data Analysts specialized in Data Quality. These roles ensure reliable, actionable insights across product analytics, financial reconciliation, and experiment measurement by embedding measurement quality into instrumentation, data modeling, testing, and reporting. The candidates will own end-to-end data integrity, governance, and cross-functional collaboration to drive data-driven decisions.
Key Responsibilities

  • Data Modeling & Reconciliation (Cross-Functional): Build and maintain semantic data models linking operational sources (ticketing, retail, inventory, marketing) to finance systems and product metrics. Engineer reconciliation processes comparing operational datasets to gold sources (general ledger, ERP) and define acceptable variances with automated outlier alerts.
  • Instrumentation QA & Data Quality Automation: Audit and validate analytics instrumentation (web/app) end-to-end: data layer → Tag Manager → GA4 (or Mixpanel/Amplitude) → data warehouse → BI. Validate event firing, privacy settings, and campaign tagging (UTMs, pixels). Develop automated data quality tests (freshness, completeness, uniqueness, reconciliation) using SQL (Snowflake, BigQuery, Redshift), Python, and frameworks like Great Expectations; schedule via Airflow/Snowflake Tasks and alert on failures.
  • Dashboarding & Reporting: Create and maintain dashboards in Power BI, Looker, Sigma, or Tableau that reflect accurate metrics and QA indicators (pass/fail badges). Ensure traceability of metrics and readiness for executive and stakeholder reviews.
  • Data Contracts, Governance & Documentation: Define data contracts for critical datasets (schema, nullability, validity thresholds) and implement checks to enforce contracts. Document data models, QA processes, SOPs, and runbooks; manage task/incident logs in Jira or ServiceNow; collaborate with product, engineering, marketing, finance, and operations in an Agile environment.
  • Cross-Functional Collaboration & Training: Partner with product, marketing, finance, and IT to plan measurement, drive data quality improvements, and educate business users on data governance and best practices.
  • Data Quality Assurance in Diverse Domains: Support quality across Product & Growth metrics, revenue attribution and tagging for finance, and experiment measurement pipelines; contribute to continuous improvement of data collection, storage, and processing.

Core Technical Skills

  • SQL & Python: Proficient SQL for querying/testing data in cloud data warehouses (Snowflake, BigQuery, Redshift). Python for data validation scripts and ETL/test automation; familiarity with Pandas and testing frameworks (PyTest, Great Expectations).
  • Analytics Tools: GA4 (custom events, dataLayer), Google Tag Manager (GTM) or similar for web/app tagging, and ad conversion pixels (Google Ads, Meta, TikTok, CM360). Experience with tag validation tools (GA Debugger, Tag Assistant).
  • BI & Visualization: Experience building dashboards in Power BI, Sigma, Tableau or Looker. Ability to translate raw data into clear visual reports. Familiarity with DAX/Power Query (for Power BI) or SQL-based modeling (for Sigma).
  • Programming: Proficiency SQL skills for large-scale reconciliations (Snowflake or similar). Comfortable with Python for data validation scripts and ETL jobs. Experience with libraries like Pandas for data processing and PyTest or Great Expectations for automated tests. Basic JavaScript skills for debugging tags via browser DevTools (Console/Network).
  • Web & Analytics Instrumentation: Deep familiarity with GA4, Google Tag Manager (GTM) or similar, custom events, dataLayer, and campaign tagging; basic JavaScript for debugging and tag validation.
  • Data Quality & Observability: Exposure to dbt for transformation/testing and data observability platforms (e.g., Monte Carlo) is a plus. Understanding CI/CD for data assets (Git, GitHub Actions, Jenkins).
  • Collaboration & Process: Experience with Jira or ServiceNow for issue tracking and Confluence (or internal wiki) for documentation. Strong communication skills to work with technical and non-technical stakeholders.
  • Scripting & QA: JavaScript and DevTools for debugging, with SQL/Python to automate data quality checks.

Originally posted on Himalayas

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