Data Engineer Machine Learning (m/w/d)

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

Job Description:

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