分析工程师
Analytics Engineer
作为数据仓库团队中的分析工程师，你将设计、开发和维护可重复使用、适合分析的数据模型，以支持ParentPay集团的报告、商业智能和决策制定。你将与复杂且有时文档不全的源系统合作，将业务需求转化为可靠的仓库结构和高效的转换逻辑。你将负责从调查和设计到开发、测试、部署和文档的数据工程工作。与BI同事和相关方紧密合作，你将帮助创建可信、一致且可扩展的数据资产，减少重复逻辑，使报告开发更简单、更快、更可靠。
关键职责
- 在数据仓库中设计和开发结构良好、可重复使用且适合分析的数据模型。
- 构建和维护具有清晰粒度、键、关系、业务定义和历史数据处理的事实和维度模型。
- 开发复杂的SQL查询、存储过程和视图，以转换和合并来自多个来源的数据。
- 调查不熟悉或文档不全的源系统，以理解其关系、业务规则和数据质量考量。
- 设计高效且易于维护的数据转换和加载流程，必要时采用增量加载或刷新方法。
- 核对源数据与仓库输出，识别并解决数据质量和逻辑问题。
- 与BI同事合作，了解报告、语义模型和Power BI需求，确保仓库模型适用于下游使用。
- 清晰且一致地记录数据模型、转换逻辑、假设和已知限制。
- 应用测试、源代码控制和开发标准，确保解决方案稳健、可维护且可扩展。
- 管理优先级、依赖项和交付风险，及时沟通进展并尽早提出障碍。
技能、知识和专长
- 熟练使用SQL Server，包括高级SQL开发、存储过程、视图和性能意识的查询设计。
- 有为报告和分析设计和开发数据仓库模型的经验。
- 熟悉维度建模概念，包括事实、维度、粒度、键和关系。
- 有从多个源系统转换、合并和核对数据的经验。
- 能够进行调查
查看英文原文
As an Analytics Engineer within the Data Warehouse team, you will design, develop and maintain reusable, analytics-ready data models that support reporting, business intelligence and decision-making across ParentPay Group. You will work with complex and sometimes imperfectly documented source systems, translating business requirements into reliable warehouse structures and efficient transformation logic.You will take ownership of data engineering work from investigation and design through development, testing, deployment and documentation. Working closely with BI colleagues and stakeholders, you will help create trusted, consistent and scalable data assets that reduce duplicated logic and make reporting development easier, faster and more reliable.
Key Responsibilities
- Design and develop well-structured, reusable and analytics-ready data models within the data warehouse.
- Build and maintain fact and dimension models with clear grain, keys, relationships, business definitions and historical data handling.
- Develop complex SQL queries, stored procedures and views to transform and combine data from multiple sources.
- Investigate unfamiliar or poorly documented source systems to understand relationships, business rules and data-quality considerations.
- Design efficient and maintainable data transformation and loading processes, including incremental load or refresh approaches where appropriate.
- Reconcile source data with warehouse outputs, identifying and resolving data-quality or logic issues.
- Work with BI colleagues to understand reporting, semantic model and Power BI requirements, ensuring warehouse models are practical for downstream use.
- Document data models, transformation logic, assumptions and known limitations clearly and consistently.
- Apply testing, source control and development standards to ensure solutions are robust, maintainable and scalable.
- Manage priorities, dependencies and delivery risks, communicating progress and raising blockers early.
Skills, Knowledge and Expertise
- Strong SQL Server experience, including advanced SQL development, stored procedures, views and performance-aware query design.
- Experience designing and developing data warehouse models for reporting and analytics.
- Good understanding of dimensional modelling concepts, including facts, dimensions, grain, keys and relationships.
- Experience transforming, combining and reconciling data from multiple source systems.
- Ability to investigate complex or poorly documented datasets and establish trusted business logic.
- Understanding of data quality, testing, documentation and maintainable engineering practices.
- Experience working with BI teams, reporting requirements and downstream analytical or semantic models.
- Ability to take ownership of work, manage priorities and deliver to agreed timescales.
- Clear communication skills, with the ability to explain technical concepts to both technical and non-technical stakeholders.
Desirable Skills and Experience
- Experience supporting Power BI reporting, semantic models or other BI and analytics platforms.
- Experience working in a SaaS, EdTech, payments or education-focused environment.
- Knowledge of source control, deployment processes and data engineering development standards.
- Experience improving existing data warehouse logic, reducing duplication and increasing model reusability.
Originally posted on Himalayas