数据仓库与BI工程师
Data Warehouse & BI Engineer
LearnTastic 正在寻找一位积极进取、注重结果的数据仓库与商业智能工程师加入我们的团队。该职位将负责构建和维护一个集中式数据仓库和商业智能平台，整合我们各个品牌、营销渠道、支付系统和实验平台的数据。
理想的候选人应具备动手能力，善于分析，有数据工程、云数据仓库、转换框架和商业智能开发方面的经验。在此职位上取得成功需要扎实的 SQL 和数据建模技能，熟悉 AWS 和 dbt，并能够将复杂的数据转化为可靠的业务洞察。
主要职责与工作内容：
- 使用 AWS Redshift Serverless 和 S3 设计和维护集中式数据仓库。
- 从 Aurora 数据库和外部 API（包括 Google Ads、Meta Ads、Microsoft Ads、Stripe、AWIN、LinkConnector 和 VWO）构建安全的数据管道。
- 使用 Aurora Zero-ETL、AWS DMS/CDC 和基于 API 的集成实现生产环境安全的数据采集。
- 开发和维护 dbt 转换模型，包括测试、文档和数据血缘。
- 为销售、市场和财务部门构建维度数据模型和数据集市。
- 开发和维护 Power BI 仪表盘、语义模型和 DAX 度量值。
- 构建连接订单、支付、退款、争议、费用和结算的财务对账模型。
- 在各品牌和渠道之间实现客户身份识别和营销归因。
- 建立自动化的数据质量检查，包括重复数据、缺失数据、过时数据源和对账问题。
- 使用 Git/GitHub 维护版本控制的数据项目。
- 与内部利益相关者合作，定义 KPI、报告需求和分析解决方案。
任职要求：
- 计算机科学、数据工程、分析或相关领域的学士学位优先。
- 3 年以上数据工程、商业智能、分析工程或数据仓库相关工作经验。
- 扎实的 SQL 和数据建模技能。
- 熟悉 AWS、Redshift 和 S3。
- 有 dbt 和现代数据转换实践的经验。
- 熟练使用 Power BI 和 DAX。
- 有构建基于 API 的数据管道和集成第三方平台的经验。
- 熟悉 AWS Aurora、DMS/CDC、Python 和 Git/GitHub。
- 对数据质量、测试、安全和数据治理有深入理解。
- 出色的分析、解决问题和沟通能力。
优先条件：
查看英文原文
LearnTastic is seeking a motivated, results-driven Data Warehouse & BI Engineer to join our growing team. This role will be responsible for building and maintaining a centralized data warehouse and Business Intelligence platform that consolidates data across our brands, marketing channels, payment systems, and experimentation platforms.
The ideal candidate is hands-on, analytical, and experienced in data engineering, cloud data warehouses, transformation frameworks, and BI development. Success in this role requires strong SQL and data modeling skills, experience with AWS and dbt, and the ability to turn complex data into reliable business insights.
Essential Duties & Responsibilities:
- Design and maintain a centralized data warehouse using AWS Redshift Serverless and S3.
- Build secure data pipelines from Aurora databases and external APIs including Google Ads, Meta Ads, Microsoft Ads, Stripe, AWIN, LinkConnector, and VWO.
- Implement production-safe ingestion using Aurora Zero-ETL, AWS DMS/CDC, and API-based integrations.
- Develop and maintain dbt transformation models, testing, documentation, and data lineage.
- Build dimensional data models and data marts for Sales, Marketing, Finance.
- Develop and maintain Power BI dashboards, semantic models, and DAX measures.
- Build financial reconciliation models connecting orders, payments, refunds, disputes, fees, and payouts.
- Implement customer identity and marketing attribution across brands and channels.
- Establish automated data quality checks for duplicates, missing data, stale feeds, and reconciliation issues.
- Maintain version-controlled data projects using Git/GitHub.
- Partner with internal stakeholders to define KPIs, reporting requirements, and analytical solutions.
Qualifications:
- Bachelor's degree in Computer Science, Data Engineering, Analytics, or related field preferred.
- 3+ years of experience in Data Engineering, BI, Analytics Engineering, or Data Warehousing.
- Strong SQL and data modeling skills.
- Hands-on experience with AWS, Redshift, and S3.
- Experience with dbt and modern data transformation practices.
- Strong experience with Power BI and DAX.
- Experience building API-based data pipelines and integrating third-party platforms.
- Familiarity with AWS Aurora, DMS/CDC, Python, and Git/GitHub.
- Strong understanding of data quality, testing, security, and data governance.
- Excellent analytical, problem-solving, and communication skills.
Preferred Qualifications
- Experience with Stripe, Google Ads, Meta Ads, Microsoft Ads, AWIN, LinkConnector, or VWO.
- Experience with e-commerce, marketing attribution, or financial reconciliation.
- Experience working with multi-brand or high-volume transactional data.
- Familiarity with customer identity resolution and cross-channel attribution.
What Success Looks Like
- Builds a reliable centralized source of truth across all LearnTastic brands.
- Delivers accurate and timely data for Sales, Marketing, Finance, and Leadership.
- Maintains strong data quality and production workload isolation.
- Develops scalable pipelines, models, and Power BI reporting.
- Improves visibility into revenue, marketing performance, user behavior, and experimentation.
Location: Remote
Project Duration: 4 months
Originally posted on Himalayas