Data Engineer at beumergroup1 – Qingpu | Jobyssey

Job Responsibility:

Build and manage the data foundation that makes AI accessible and scalable across the organization.
Enable seamless integration of enterprise data through APIs, middleware, and data pipelines.
Deliver trusted, high-quality, and AI-ready data assets for analytics, automation, and AI solutions.
Create scalable data products, semantic models, and integration frameworks that simplify data consumption.
Connect business applications, platforms, and external services to provide unified and accessible enterprise data.
Design, develop, and maintain enterprise data integrations and API-based data exchange.
Build and operate scalable data pipelines that provide reliable access to business-critical data.
Integrate ERP, CRM, HR, Finance, Manufacturing, and external systems into the enterprise data platform.
Manage and optimize data ingestion, transformation, and delivery processes.
Develop and maintain semantic models that make data understandable and consumable for AI and business users.
Ensure data quality, lineage, consistency, and governance across integrated systems.
Create reusable integration patterns, data products, and data services.
Monitor, troubleshoot, and continuously improve integration and pipeline performance.
Support reporting, analytics, automation, and AI initiatives with trusted and accessible datasets.
Collaborate with business stakeholders to identify and prioritize data integration opportunities.
Contribute to the evolution of the enterprise data architecture and AI data platform roadmap.
Maintain technical documentation, standards, and best practices for integrations and data assets.

Job Requirements:

Bachelor's degree with majored in computer or data science or similar field.
> 3 years experience in data engineering / science.
Strong experience in Data Engineering and Enterprise Data Integration.
Expertise in API development and integration (REST, OData).
Experience with Boomi or similar Integration Platform-as-a-Service (iPaaS) solutions.
Advanced SQL and data transformation skills.
Experience designing and operating ETL/ELT pipelines.
Knowledge of Snowflake or similar database and/or data lake solutions.
Understanding of data lake, data warehouse, and modern data platform architectures.
Experience with semantic data modeling and business data abstraction layers.
Knowledge of JSON, XML, Parquet and further common data formats.
Experience with source control, CI/CD, and DevOps practices.
Understanding of data governance, security, privacy, and compliance principles.
Experience supporting AI and machine learning use cases through data engineering.
Familiarity with event-driven architectures, messaging, and real-time data integration.
Passion for making data accessible, understandable, and usable for AI and business value creation.
Strong analytical and systems-thinking capabilities.
Ability to translate complex business requirements into scalable data solutions.
Strong stakeholder engagement and communication skills.
Collaborative mindset with the ability to work across business and technology functions.
Ownership mentality with accountability for end-to-end data solutions.
Strong problem-solving and troubleshooting capabilities.
Curiosity for emerging AI, data, and integration technologies.

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