Senior Data AI Engineer

• 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-premiseand cloud) incorporating automation observability and resiliency by design

• Designs builds and enhances large-scale data processing systems and data lakes on Google Cloud Platformoptimizing forcomputational and storage efficiency while applying strongexpertisein 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 provideexpertiseto supportsubsequentrelease testing.

• Actively adheres toestablishedquality and reliability standards and ensures team adheres to the same quality and standards working in an Agile development environment.

• Researchidentifiesandimplementsprocess 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;establishingpersonal networks;participatingin professional societies. Drives the evolution of CNA application development processes and standards.

• 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.

• Proficiencyin 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 asBigQuery Cloud Storage DataflowDataproc Pub/Sub and Cloud Composer or equivalent cloud-native data technologies.

• Preferred experience in insurance and financial services including familiarity with regulatory risk underwriting claims customer and operational data domains.

• Experience with big data and distributed processing technologies hybrid cloud andon-premiseenvironments and modern engineering practices including automation testing CI/CD and secure data handling.

• Working knowledge of business intelligence reporting and analytics enablement tools with an understanding of data governance lineage monitoring and controls needed to support trusted production-grade data products.

Education & Experience

• Bachelors degree withMasterspreferred in Computer Science Information Technology relateddisciplineor equivalent work experience.

• Typically5 years of experience in dataanalyticsor application development.

• 2 years of codingproficiencyin at least one programming language (Python Java SQL).

• Experience using Agile methods preferred.

• Applicable certifications preferred (GCP Data Engineering).

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