ContiTech USA, Inc. is hiring a Senior Software Engineer – Data Platform (Tech Lead)

▪ 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.