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Senior Data AI Engineer

CNA

  • Chicago, Illinois
  • 3 days ago
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    Skills

    • Agile Programming Methodologiesunmatched
    • Algorithmsunmatched
    • Analysis Skillsunmatched
    • Apache Sparkunmatched
    • Artificial Intelligence (AI)unmatched
    • Big Dataunmatched
    • Cataloguingunmatched
    • Cloud Computingunmatched
    • Communication Skillsunmatched
    • Computer Scienceunmatched
    • Data Managementunmatched
    • Data Martunmatched
    • Data Migrationunmatched
    • Data Miningunmatched
    • Data Processingunmatched
    • Data Qualityunmatched
    • Data Scienceunmatched
    • Database Designunmatched
    • Deep Learningunmatched
    • DevOpsunmatched
    • Distributed Computingunmatched
    • Document Imagingunmatched
    • GCP (Good Clinical Practices)unmatched
    • Information Retrievalunmatched
    • Insuranceunmatched
    • Interpersonal Skillsunmatched
    • Javaunmatched
    • Leadershipunmatched
    • Machine Learningunmatched
    • Mathematicsunmatched
    • Natural Language Processing (NLP)unmatched
    • People Managementunmatched
    • Process Improvementunmatched
    • Programming Languagesunmatched
    • Python Programming/Scripting Languageunmatched
    • SQL (Structured Query Language)unmatched
    • Semantic Searchunmatched
    • Software Engineeringunmatched
    • Statisticsunmatched
    • Structured Dataunmatched
    • System Migrationunmatched
    • Test Automationunmatched
    • Training Data Setsunmatched
    • Unstructured Dataunmatched

    Description

    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. 

    A senior individual contributor role responsible for designing, building, and operationalizing end-to-end AI and machine learning solutions that accelerate CNA's migration to a modern cloud data lakehouse. The engineer works across structured and unstructured data domains — including documents, images, audio, and transactional records — to unlock analytical value through scalable pipelines, RAG architectures, vector databases, and knowledge graphs. This role may also provide guidance to others to support the building of complex technical capabilities.

    JOB DESCRIPTION:

    Essential Duties & Responsibilities

    Performs a combination of duties in accordance with departmental guidelines:

    • Design and build AI solutions that accelerate data migration from legacy systems to the cloud, ensuring scalability, reliability, and governance compliance. 

    • Design and implement scalable ingestion and transformation pipelines across structured (SQL, relational) and unstructured (documents, images, audio, email, call transcripts) data sources, applying OCR, NLP preprocessing, and document chunking strategies optimized for LLM consumption. 

    • Implement modern lakehouse patterns on Google Cloud Platform (GCP) — including data governance, cataloging, and lineage tracking — to ensure data is reliably discoverable, auditable, and fit for AI/ML workloads at scale.  

    • Design and implement vector databases, embedding pipelines, and knowledge graph structures that serve as the foundational retrieval layer for RAG and other AI applications. 

    • Productionize and operationalize AI solutions and advanced analytics in a DevOps/MLOps environment, including automated testing, monitoring, and rollback capabilities. 

    • Cultivate innovation by proactively proposing new ideas and identifying the right combination of tools and frameworks to turn business problems into analytics solutions. 

    • Researches, identifies and implements process improvements that address complex technology gaps. Builds strong knowledge of technology enablers. 

    May perform additional duties as assigned.

    Reporting Relationship

    Typically Director or above

    Skills, Knowledge & Abilities

    • Deep expertise building scalable ingestion and transformation pipelines across structured and unstructured data sources; strong background migrating workloads from legacy systems to modern cloud platforms.  

    • Skilled in parsing and normalizing diverse content types — PDFs, emails, images, and call transcripts — using OCR, NLP preprocessing (tokenization, entity extraction, summarization), and document chunking strategies optimized for LLM consumption. 

    • Proven experience designing and implementing vector databases (e.g., Vertex AI Vector Search, Pinecone, pgvector), embedding pipelines, and knowledge graph structures that underpin RAG and semantic search applications 

    • Strong SQL and data analytical skills; experience building data marts and feature datasets for data science and ML applications. 

    • Strong coding fluency in Python; hands-on experience with BigQuery, Claude Code, RAG architectures, LLMs, ADK, and prompt engineering techniques 

    • Expertise in building ML platforms and data pipelines at scale; familiarity with major ML algorithms, deep learning, NLP, information retrieval, and data mining techniques 

    • Experience with GCP services (Vertex AI, Dataflow, BigQuery, Cloud Run, Pub/Sub); comfort with distributed computing frameworks (Apache Spark, Dataproc) for large-scale data processing. 

    • Solid experience managing diverse data sources including preprocessing, cleansing, and verifying data integrity to meet data science and ML requirements 

    • Demonstrated experience with machine learning, deep learning, information retrieval, NLP, or data mining — particularly applied to unstructured or semi-structured data  

    • Hands-on experience with vector databases, embedding models (e.g., text-embedding-gecko, OpenAI Ada, Cohere), and end-to-end RAG pipeline design  

    • Experience using Agile methods preferred.   

    • Strong communication and interpersonal skills and the ability to work effectively with peers and team members in a highly matrixed environment. 

    • Preferred experience with the insurance industry, its products and services. 

    • Experience in implementing big data processing technology. Apache Spark preferred. 

    Education & Experience

    • Bachelor's Degree in Computer Science, Engineering, Mathematics, Computational Statistics, Data Science, or a related technical field (or equivalent experience); Master's Degree preferred. 

    • Typically 7+ years of experience in data engineering, Artificial Intelligence or Machine Learning. 

    • 2+ years of coding proficiency in at least one programming language (Python, Java, SQL). 

    • Applicable certifications preferred (GCP, Data Engineering). 

    #LI-KJ1 #LI-HYBRID

    In certain jurisdictions, CNA is legally required to include a reasonable estimate of the compensation for this role. In District of Columbia,California, Colorado, Connecticut, Illinois,Maryland, Massachusetts, New York and Washington,the national base pay range for this job level is $72,000 to $141,000 annually. Salary determinations are based on various factors, including but not limited to, relevant work experience, skills, certifications and location. CNA offers a comprehensive and competitive benefits package to help our employees – and their family members – achieve their physical, financial, emotional and social wellbeing goals.  For a detailed look at CNA’s benefits, please visit cnabenefits.com.


    CNA utilizes AI-enabled technology during the recruiting process. For more information, please visit our careers page.


    CNA is committed to providing reasonable accommodations to qualified individuals with disabilities in the recruitment process. To request an accommodation, please contact 

    leaveadministration@cna.com

    Numbers & Facts

    LocationChicago, Illinois
    IndustryOther/Not Classified
    Company Size100 to 499 employees
    Year Founded1940
    Websitehttps://www.cna.org/

    About Company

    CNA's approach to research is a modern iteration of the Newtonian principle that complex, dynamic processes are best understood through direct observation of events and people. That was the methodology CNA analysts first applied in the 1940s when they pioneered the field of operations research by helping the Navy address the German U-boat threat. Not content to study the problem from afar, this small group of MIT scientists insisted on deploying with Navy forces in order to observe operations and collect the data needed for meaningful analyses. Their groundbreaking work, and the anti-submarine warfare equations it produced, set a standard for operations research methods that CNA has maintained for 75 years. Today, with more than 500 professionals at our headquarters and 50 researchers in the field, CNA still takes a multi-disciplinary, real-world approach to our work. On-site analysts carefully observe all aspects of a process—people, decisions, actions, consequences—and then collaborate with a headquarters-based research team to assess data and arrive at findings. CNA's objective, empirical research and analysis helps decision makers develop sound policies, make better-informed decisions, and manage programs more effectively. Our work, which in its early decades focused solely on defense-related matters, has grown to include investigation and analysis of a broad range of national security, defense, and public interest issues including education, homeland security and air traffic management. Through our Center for Naval Analyses and Institute for Public Research, we provide public-sector organizations with the tools they need to tackle the complex challenges of making government more efficient and keeping our country safe and strong.

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