数据分析师
Data Analyst
人工智能。自动化。云工程。高级分析。
对于企业而言，这些是成功的关键因素。对我们而言，它们是我们核心的专业领域。
我们与全球知名品牌合作。我们通过领先的科技和卓越的业务流程，为他们带来独特的价值主张。其中心是数字工程——推动快速创新和可扩展业务转型的基础。
我们已创造363项独特且独立的发明，其中250项基于人工智能，并在关键科技领域获得多项专利。借助我们的先进产品和平台，我们实现大规模的数字化转型，优化关键业务运营，重塑体验，开创新解决方案，所有服务均通过无缝的“即服务”模式提供。
我们为每家公司提供新的业务钥匙，包括他们所合作的人、以及他们服务的客户。凭借经验证的战略和敏捷执行，我们不仅推动变革——我们打造数字成果。
Sutherland
数字成果。
数据工程与数据湖开发
· 使用 Microsoft Fabric Data Factory 设计和开发数据管道。
· 在 Microsoft Fabric 内创建和维护 Lakehouse 和 Warehouse 解决方案。
· 实现并管理 Bronze、Silver 和 Gold 层架构。
· 开发可扩展的 ETL/ELT 管道，用于从多个来源获取和转换数据。
· 监控管道性能并排查数据质量问题。
数据建模
· 按照维度建模原则设计和开发事实表和维度表。
· 创建和维护星型模式和雪花模式数据模型。
· 在需要时定义代理键、业务键和缓慢变化维度（SCD）。
· 优化数据模型以提升报表和分析性能。
SQL 开发
· 编写复杂 SQL 查询，用于数据提取、转换和验证。
· 构建视图、存储过程和可重用查询框架。
· 执行查询优化和性能调优。
Python 开发
· 开发基于 Python 的数据转换和验证脚本。
· 使用库如：· Pandas
· NumPy
· PySpark
· Requests
- 自动化数据质量检查和对账流程。
- 分析与报告
- 与业务相关方合作，收集报告和分析需求。
- 开发适用于 Power BI 报告的数据集。
- 支持 KPI、指标和业务数据定义的创建。
- 确保
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Artificial Intelligence. Automation. Cloud Engineering. Advanced Analytics.
For Enterprises, these are key factors of success. For us, they're our core expertise.
We work with global iconic brands. We bring them a unique value proposition through market-leading technologies and business process excellence. At the heart of it all is Digital Engineering - the foundation that powers rapid innovation and scalable business transformation.
We've created 363 unique and independent inventions, 250 of which are AI-based and rolled up under several patent grants in critical technologies. Leveraging our advanced products and platforms, we drive digital transformation at scale, optimize critical business operations, reinvent experiences, and pioneer new solutions, all provided through a seamless "as-a-service" model.
For each company, we provide new keys for their businesses, the people they work with, and the customers they serve. With proven strategies and agile execution, we don't just enable change - we engineer digital outcomes.
Sutherland
Digital Outcomes.
Data Engineering & Fabric Development
· Design and develop data pipelines using Microsoft Fabric Data Factory.
· Create and maintain Lakehouse and Warehouse solutions within Microsoft Fabric.
· Implement and manage Bronze, Silver, and Gold Layer architectures.
· Develop scalable ETL/ELT pipelines for ingesting and transforming data from multiple sources.
· Monitor pipeline performance and troubleshoot data quality issues.
Data Modeling
· Design and develop Fact Tables and Dimension Tables following dimensional modeling principles.
· Create and maintain Star Schema and Snowflake Schema data models.
· Define surrogate keys, business keys, and Slowly Changing Dimensions (SCD) where required.
· Optimize data models for reporting and analytics performance.
SQL Development
· Write complex SQL queries for data extraction, transformation, and validation.
· Build views, stored procedures, and reusable query frameworks.
· Perform query optimization and performance tuning.
Python Development
· Develop Python-based data transformation and validation scripts.
· Use libraries such as:· Pandas
· NumPy
· PySpark
· Requests
- Automate data quality checks and reconciliation processes.
- Analytics & Reporting
- Partner with business stakeholders to gather reporting and analytics requirements.
- Develop datasets optimized for Power BI reporting.
- Support creation of KPIs, metrics, and business data definitions.
- Ensure data governance and consistency across analytical solutions.
Our most successful candidates will have:
· 3-7 years of experience in Data Analytics, Data Engineering, or Business Intelligence.
· Minimum 2 years of hands-on experience with Microsoft Fabric or Azure-based analytics platforms.
· Proven experience building:· Fact Tables
· Dimension Tables
· Data Pipelines
· Power BI datasets
- Experience with Microsoft Azure Data Services.
- Experience with Delta Tables and Apache Spark.
- Familiarity with CI/CD and Git-based development.
- Understanding of data governance and lineage concepts.
- Microsoft Fabric Certification preferred:· DP-600
- DP-700