ContiTech USA, Inc. is hiring an IT engineer Data Engineering

Design, implement, and maintain secure and scalable data ingestion pipelines from a wide variety of source systems, including SAP, SalesForce, SharePoint, APIs, and (legacy) manufacturing platforms. Build and enhance metadata-driven services that enable discoverability, access governance, and operational transparency of enterprise data. Serve as a technical expert and cross-functional enabler for structured and unstructured data acquisition, quality, and compliance. Establish and maintain holistic data quality management, monitoring and reporting. Contributes to a global data engineering team delivering to all major business domains. Drives ingestion and metadata service implementation for over 100 enterprise data sources. Collaborates across business IT, cybersecurity, infrastructure, and architecture teams to ensure secure and sustainable delivery. Main Tasks: ▪ Build and maintain Python- or Scala-based extraction services (e.g., Debezium Server, custom APIs, rclone) • Implement CDC, delta, and event-based patterns. • Push-based HTTP, Kerberos-authenticated DLT delivery. • Establish, operate and troubleshoot extraction from SAP using tools like Theobald Extract Universal.o • Integrate with systems such as Salesforce, SharePoint, and other API- or file-based endpoints. ▪ Establish and maintain a business-friendly, web-accessible data catalog application, with dataset profiles, metadata, and usability features. ▪ Integrate dataset discoverability, preview/exploration options, and lineage information using Unity Catalog as a backend metadata system. ▪ Design and implement structured access request workflows including request submission, approval chains, audit trail, and enablement triggers. • Perform design reviews with Cybersecurity. • Ensure documentation and compliance for all interfaces and data ingress points. • Manage audit and traceability requirements. • Collaborate closely with IT and business users to translate requirements into scalable technical patterns. • Serve as technical escalation point for complex source integration. ▪ Define and implement a multi-layered data quality framework, including unit-level, integration-level, and cross-pipeline validation rules. ▪ Establish centralized and version-controlled storage of DQ rules, with integration into orchestration and CI/CD pipelines. ▪ Implement automatic DQ monitoring with severity levels (Critical, High, Medium, Low) and enable flagging, filtering, and quarantining logic at relevant stages of the pipeline. ▪ Collaborate with source system owners and business stakeholders to define meaningful and actionable DQ thresholds.