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.