Senior Data AI Engineer

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/No
  
  SQL 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 No
  
  SQL 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 Big Query, Cloud Storage, Dataflow, Dataproc, Pub/Sub, and Cloud Composer, or equivalent cloud-native data technologies.

• Preferred experience…