Senior Data AI Engineer

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You have a clear vision of where your career can go. And we have the leadership to help you get there. At CNA, we strive to create a culture in which people know they matter and are part of something important, ensuring the abilities of all employees are used to their fullest potential. Senior Data/AI Engineer is an individual contributor role responsible for designing, building, and modernizing production-grade data pipelines and data solutions across hybrid cloud and on-premise environments. The role applies expertise in AI-assisted development tools, strong engineering judgment, and modern data engineering practices to integrate structured, semi-structured, and unstructured data across complex enterprise ecosystems, leveraging both legacy platforms and cloud-native technologies to deliver scalable, secure, resilient, and high-quality solutions that improve speed to delivery, operational reliability, and business value. JOB DESCRIPTION: Essential Duties & Responsibilities Performs a combination of duties in accordance with departmental guidelines: Serves as a key team member who delivers results and creates value for the CNA brand, customers, and internal stakeholders, while collaborating effectively with external and offshore resources as needed. Demonstrate hands-on experience using AI-assisted development tools to accelerate engineering tasks such as pipeline creation, code generation, testing, and troubleshooting Apply strong engineering judgment when working with AI-generated outputs, ensuring alignment with enterprise standards for quality, security, and data handling Design and build data pipelines that support multi-modal data, including structured, semi-structured, and unstructured sources (e.g., transactional data, documents, and external data feeds) Build and modernize data pipelines across hybrid environments ( on-premise and cloud), incorporating automation, observability, and resiliency by design Designs, builds, and enhances large-scale data processing systems and data lakes on Google Cloud Platform, optimizing for computational and storage efficiency while applying strong expertise in data modeling and engineering best practices. Operate effectively in complex, multi-entity and multi-national system landscapes, integrating internal platforms and external data providers Bring familiarity with both legacy enterprise data tools (e.g., ETL/ELT platforms, relational databases) and modern cloud-native data and integration services This role is not focused on experimentation alone - it is focused on applying AI in a disciplined, production-grade engineering environment to drive measurable improvements in delivery speed, quality, and operational reliability May lead or sub-lead the design of complex physical data models, projects and cloud-based data lake constructs including SQL/NoSQL database systems. May lead or sub-lead the creation of integrated data views based on business or analytics requirements. May lead or sub-lead robust unit testing to ensure deliverables match the design and provide expertise to support subsequent release testing. Actively adheres to established quality and reliability standards, and ensures team adheres to the same quality and standards working in an Agile development environment. Research, identifies and implements process improvements that address complex technology gaps. Builds strong knowledge of technology enablers. May lead or sub-lead the design and building of data solutions and applications that enable reporting, analytics, data science, and data management. Maintains professional and technical knowledge by attending educational workshops; reviewing professional publications; establishing personal networks; participating in professional societies. Drives the evolution of CNA application development processes and standards. May perform additional duties as assigned. Reporting Relationship Typically Director or above Skills, Knowledge & Abilities Strong knowledge of data architecture, relational and NoSQL database concepts, ETL/ELT patterns, dimensional modeling, metadata, and data quality frameworks for enterprise-scale data solutions. Strong experience designing and building scalable data integration and pipeline solutions with a focus on accuracy, observability, resiliency, performance optimization, and ease of consumption. Proficiency in Python and advanced SQL for large-scale, complex datasets, with hands-on experience using AI-assisted development tools to accelerate coding, testing, troubleshooting, and engineering productivity. Strong communication , collaboration, and stakeholder engagement skills, with the ability to apply sound engineering judgment and work effectively across highly matrixed, cross-functional, and global teams. Preferred experience building data and analytics solutions on Google Cloud Platform, including services such as BigQuery , Cloud Storage, Dataflow, Dataproc , Pub/Sub, and Cloud Composer, or equivalen

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