Machine Learning-Jobs in Malaysia

Job Role: Data Engineer

Location: Frankfurt Germany (Remote with Occasional travel to Frankfurt)

Job Type: Permanent/Contract

Language: Must be a Fluent German and English speaker

Candidate must be based in Germany

1. 4-6 years of experience in various data architecture and engineering roles within data & analytics.
2. Collaborate with stakeholders to understand and document data requirements, business rules, and objectives for the data platform.
3. Design and develop conceptual, logical, and physical data models that accurately represent the organization's data assets and support its business needs.
4. Ensure designs meet documented objectives for reliability, scalability, supportability, user experience, security, governance, performance and more.
5. Implement data engineering practices, including normalization, denormalization, and indexing, to ensure data integrity, performance, and scalability.
6. Work closely with architects to integrate data engineering into the overall platform architecture, ensuring efficient data processing and storage.
7. Communicate effectively with technical and non-technical stakeholders to present and explain data engineering, design decisions, and recommendations.
8. 3+ years of hands-on relational, dimensional, and/or analytic experience (using RDBMS, dimensional, NoSQL data platform technologies, and ETL and data ingestion protocols).
9. Knowledge of Sagemaker/Jupyter, and data lake.
10. Experience with data warehouse, data lake, and enterprise big data platforms in multi-data-center contexts required.
11. Implement business and IT data requirements through new data strategies and designs across all data platforms (relational, dimensional, and NoSQL) and data tools (reporting, visualization, analytics, and machine learning).
12. Identify the architecture, infrastructure, and interfaces to data sources, tools supporting automated data loads, security concerns, analytic models, and data visualization.
13. Hands-on modeling, design, configuration, installation, performance tuning, and sandbox POC.
14. Ensuring that data are modelled and processed according to architecture and requirements both functional and non-functional.
15. Understanding and implementing required development guidelines, design standards and best practices.
16. Delivering right solution architecture, automation and technology choices.
17. Working cross-functionally with enterprise architects, information security teams, and platform teams.
18. Suggesting and implementing architecture improvements.