体育与媒体分析工程师
Analytics Engineer – Sports & Media Analytics
工作标题：高级分析工程师 (DBT,Snowflake,SQL) 经验：8年至10年 地点：远程就业类型：合同/职位摘要 我们正在寻找一个有经验的高级分析工程師，拥有 dbt,SQL,Snowflake 和数据仓库方面的强大的专业知识来设计，开发和优化可扩展的分析数据模型和转换管道。理想候选人将具有广泛的经验，建立准备好做生意的数据集，维度模型和支持报告，BI 和先进分析举措的分析解决方案。必需技能 · 8年以上在数据工程 /分析工程领域的经验 · 3年以上与 dbt 的实践经验 · 高级 SQL 强大的专业知识 · 雪强大的经验 · Amazon Redshift 的经验 · 维度数据建模 （星球计划，雪方案）的专业知识 • 数据存储概念的经验 • 济 inja 模板的知识 · Python 的实操了解 · Git,CI/CD 和敏捷方法的经验· 实施数据质量，测试和治理框架的主要责任分析工程和数据建模 · 使用 SQL,dbt 和 Jinja 模板设计，开发和维护可扩展的数据转换管道。· 建立和维护强大的尺寸数据模型，以支持分析，报告和业务智能。·开发可重复使用的 dbt 模型，宏观程序，包装，测试和文档。· 将业务要求转化为可扩展的分析数据模型。数据仓库与优化。·设计和优化分析数据市场和仓库结构。·实施量级建模，事实表和尺度表最佳做法。·利用集群，分区，物质化策略和性能调整技术优化 SQL 工作负载。·确保高性能数据管道以及在大型数据组上有效执行查询。· 实现自动化测试和验证框架。· 在报告平台中确保业务指标的一致性和准确性。·支持数据治理倡议，隐私控制和访问管理要求。·保持高标准的数据质量，可靠性和文档。协作与交付。· 与产品，分析，BI 和数据团队合作以了解业务需求。·参与敏捷仪式和活动。
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This is a remote position.
Job Title: Senior Analytics Engineer (DBT, Snowflake, SQL)
Experience: 8 to 10 Years
Location: Remote
Employment Type: Contract /
Job Summary
We are seeking an experienced Senior Analytics Engineer with strong expertise in dbt, SQL, Snowflake, and Data Warehousing to design, develop, and optimize scalable analytical data models and transformation pipelines. The ideal candidate will have extensive experience building business-ready datasets, dimensional models, and analytics solutions that support reporting, BI, and advanced analytics initiatives.
Mandatory Skills
· 8+ years of experience in Data Engineering / Analytics Engineering
- 3+ years of hands-on experience with dbt
- Strong expertise in Advanced SQL
- Strong experience in Snowflake
- Experience with Amazon Redshift
- Expertise in Dimensional Data Modeling (Star Schema, Snowflake Schema)
- Experience with Data Warehousing Concepts
- Knowledge of Jinja Templating
- Working knowledge of Python
- Experience with Git, CI/CD, and Agile methodologies
- Experience implementing Data Quality, Testing, and Governance Frameworks
Key Responsibilities
Analytics Engineering & Data Modeling
· Design, develop, and maintain scalable data transformation pipelines using SQL, dbt, and Jinja templating.
- Build and maintain robust dimensional data models to support analytics, reporting, and business intelligence.
- Develop reusable dbt models, macros, packages, tests, and documentation.
- Translate business requirements into scalable analytical data models.
Data Warehousing & Optimization
· Design and optimize analytical data marts and warehouse structures.
- Implement best practices for dimensional modeling, fact tables, and dimension tables.
- Optimize SQL workloads using clustering, partitioning, materialization strategies, and performance tuning techniques.
- Ensure high-performance data pipelines and efficient query execution on large datasets.
Data Quality & Governance
· Implement automated testing and validation frameworks within dbt.
- Ensure consistency and accuracy of business metrics across reporting platforms.
- Support data governance initiatives, privacy controls, and access management requirements.
- Maintain high standards of data quality, reliability, and documentation.
Collaboration & Delivery
· Partner with Product, Analytics, BI, and Data teams to understand business requirements.
- Participate in Agile ceremonies and sprint planning activities.
- Perform code reviews and maintain development best practices.
- Collaborate with distributed global teams for successful project delivery.
Required Technical Skills
Analytics Engineering
· dbt Core / dbt Cloud
- dbt Models
- dbt Macros
- dbt Packages
- dbt Testing
- Incremental Models
- Snapshot Management
- Documentation
Database & Data Warehouse
· Snowflake
- Amazon Redshift
- SQL Query Optimization
- Performance Tuning
- Data Warehousing
- Dimensional Modeling
Programming & Automation
· SQL (Advanced)
- Python
- Jinja Templating
DevOps & Development Practices
· Git
- CI/CD Pipelines
- Code Reviews
- Agile/Scrum
Preferred Skills
· Experience with Tableau, Power BI, or Looker
- Experience in Sports, Media, Entertainment, Consumer Analytics, or Digital Marketing domains
- Experience working with customer engagement, audience analytics, and campaign measurement data
- Experience handling clickstream, event-stream, or behavioral datasets
- Exposure to semantic layer design and metric standardization
Educational Qualification · Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Mathematics, Statistics, or a related quantitative discipline.
Originally posted on Himalayas