Continental
tire manufacturer
DataPlatformEngineer(DataIntegration)
Neural analysis suggests this role is
optimal for mid candidates.
“Data Platform Engineer (Data Integration) at Continental. Skills: Data Platform Engineering, Data Integration, Cloud-based data-virtualization platform, CI/CD, Performance Optimization, Reliability Engineering, Data Engineering, Data Modeling. Build and operate a secure, reliable cloud‑based data‑virtualization platform with automated deployment and scaling through CI/CD. Ensure high system performance and availability through monitoring, incident management, and continuous optimization”
What You'll Achieve.
accelerate Continental’s internal digitalization; deliver secure, unified, and tool-agnostic access to data - powering analytics, reporting, simulation, and AI at scale
Industry & Context.
analytical and problem-solving abilities
What They're Looking For.
Must Have
Practical experience in data engineering, data integration, analytics, or a comparable technical, Proficiency in Python and SQL, Professional proficiency in English (written and spoken)
Nice to Have
Experience or interest in data virtualization technologies (e. g. , Denodo), data warehouses, and cloud-based data platforms (preferably AWS), including basic monitoring tools (e. g. , Grafana), Curiosity, willingness to learn, and a interest in data platform and virtualization technologies.
What You'll Do.
Build and operate a secure
reliable cloud‑based data‑virtualization platform with automated deployment and scaling through CI/CD
Ensure high system performance and availability through monitoring
and continuous optimization
Develop consistent data models and collaborate with cross‑functional teams to deliver integrated data for analytics and AI
contribute to the development
and continuous improvement of our central access layer
including its automation
How You'll Work.
Team & Collaboration
collaborate with cross‑functional teams; collaborate effectively in a hybrid, multi-location team
Communication Scope
Professional proficiency in English (written and spoken)
Process & Methodology
Agile ways of working
Full Job Description
Continental is a leading tire manufacturer and industry specialist. Founded in 1871, the company generated sales of €39.7 billion in 2024 and currently employs around 95,000 people in 54 countries and markets. Tire solutions from the Tires group sector make mobility safer, smarter, and more sustainable. Its premium portfolio encompasses car, truck, bus, two-wheel, and specialty tires as well as smart solutions and services for fleets and tire retailers. Continental has been delivering top performance for more than 150 years and is one of the world’s largest tire manufacturers. In fiscal 2024, the Tires group sector generated sales of 13.9 billion euros. Continental's tire division employs more than 57,000 people worldwide and has 20 production and 16 development sites. You will be part of a versatile team of data and analytics specialists that provide IT solutions to ensure and accelerate Continental’s internal digitalization. Our team builds and operates a modern data platform and delivers services across data engineering, data architecture, machine learning engineering, and analytics. We are evolving our data platform by introducing a central access layer and enterprise data catalog that delivers secure, unified, and tool-agnostic access to data - powering analytics, reporting, simulation, and AI at scale. In this role, you will contribute to the development, stabilization, and continuous improvement of our central access layer, including its automation, operations, and reliability. Main Responsibilities: Platform Setup, Operations & Automation: * Build and operate a secure, reliable cloud‑based data‑virtualization platform with automated deployment and scaling through CI/CD. Performance Optimization & Reliability Engineering: * Ensure high system performance and availability through monitoring, incident management, and continuous optimization. Data Engineering, Modeling & Integration: * Develop consistent data models and collaborate with cross‑functional teams to
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