Continental develops pioneering technologies and services for sustainable and connected mobility of people and their goods. Founded in 1871, the technology company offers safe, efficient, intelligent, and affordable solutions for vehicles, machines, traffic and transportation. In 2023, Continental generated sales of €41.4 billion and currently employs around 200,000 people in 56 countries and markets.
Guided by the vision of being the customer's first choice for material-driven solutions, the ContiTech group sector focuses on development competence and material expertise for products and systems made of rubber, plastics, metal, and fabrics. These can also be equipped with electronic components in order to optimize them functionally for individual services. ContiTech's industrial growth areas are primarily in the areas of energy, agriculture, construction, and surfaces. In addition, ContiTech serves the automotive and transportation industries as well as rail transport.
The IT Digital and Data Services Competence Center of ContiTech caters to all the Business Areas in ContiTech and responsible among other on areas of Data & Analytics, Web and Mobile Software Development and AI
The team for Data services specializes in all platforms, business applications and products in the domain of data and analytics, covering the entire spectrum including AI, machine learning, data science, data analysis, reporting and dashboarding.
Job Description:▪ Own and govern the data ingestion and integration architecture for the enterprise data platform.
▪ Define and evolve scalable, reusable, and production-grade data interfaces across all source systems and business domains.
▪ Provide technical leadership and design authority for all data platform ingestion and integration components, ensuring engineering quality, scalability, and reliability.
▪ Establish and enforce software engineering and architecture standards across data platform development (CI/CD, testing, observability, design patterns).
▪ Act as the primary escalation point and technical authority for complex ingestion, integration, and data platform challenges.
▪ Guide and support a distributed team of software engineers and data engineers in building production-grade ingestion services and platform components.
▪ Collaborate closely with platform architecture, cybersecurity, infrastructure, and business IT teams to ensure secure, compliant, and sustainable system design.
▪ Drive consistency and reuse across data platform patterns, frameworks, and engineering practices.
Main Tasks:
▪ Define and evolve ingestion frameworks for batch, streaming, API-based, and event-driven architectures.
▪ Establish and govern standards for structured and unstructured data (JSON, CSV, XML), including schema evolution and compatibility strategies.
▪ Design and review production-grade ingestion and integration patterns, ensuring fault tolerance, observability, and performance.
▪ Define and enforce API design standards, reliability patterns, and contract management.
▪ Lead design reviews for high-risk or complex ingestion pipelines, focusing on scalability, security, and maintainability.
▪ Support engineers in implementing robust error handling, retry mechanisms, orchestration, and monitoring.
▪ Collaborate with system owners to define interface specifications and integration strategies.
▪ Align ingestion architecture with overall platform design, governance, and business requirements.
▪ Enable ingestion patterns for advanced use cases, including data science, machine learning, and AI/LLM integrations.
▪ Maintain and evolve reusable libraries, templates, and framework components to accelerate development.
▪ Promote engineering best practices, including code reviews, testing strategies (unit, integration), and CI/CD pipelines.
▪ Define and maintain architecture blueprints, design guidelines, and engineering standards.
▪ Create reusable architecture patterns for lakehouse-based data platform.
▪ Provide guidance on scalability strategies, resource utilization, and performance optimization.
▪ Support cost transparency, usage optimization, and efficient resource consumption across the platform.
Qualifications:
Qualifications
▪ Degree in Computer Science or a related field.
▪ 6–10 years of experience in software engineering and data platform development, with a focus on large-scale, enterprise-grade systems.
▪ Proven track record of technical leadership and architectural ownership in distributed systems or data platforms.
▪ Strong hands-on experience with Scala / Java and Python in a production software engineering environment.
▪ Deep understanding of data integration patterns (APIs, CDC, streaming, event-driven architectures).
▪ Experience designing and governing reusable frameworks, platform components, and engineering standards.
▪ Strong knowledge of CI/CD, version control, automated testing, and observability practices.
▪ Experience with Microsoft Azure or other hyperscaler environments.
▪ Familiarity with lakehouse and data warehousing architectures; Databricks experience is a plus.
▪ Ability to guide and mentor engineers while collaborating across global teams and stakeholders.
NOT a fit if your experience is primarily:
▪ Notebook-based data processing (e.g. Jupyter, ad-hoc PySpark) without production deployment
▪ Dashboarding / analytics / BI tools
▪ SQL-only roles without software engineering experience
Additional Information:
The well-being of our employees is important to us. That's why we offer exciting career prospects and support you in achieving a good work-life balance with additional benefits such as:
- Training opportunities
- Mobile and flexible working models
- Sabbaticals
and much more...
Sounds interesting for you? Click here to find out more.
Diversity, Inclusion & Belonging are important to us and make our company strong and successful. We offer equal opportunities to everyone - regardless of age, gender, nationality, cultural background, disability, religion, ideology or sexual orientation.
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