Senior Software Engineer - Data Platform (Tech Lead)

Requirements

• ▪ 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
• 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.
• Ready to drive with Continental? Take the first step and fill in the online application.

Benefits

Additional Information

▪ 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.

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