Sr Data Engineer - Hybrid

Overview

Sr Staff Data Engineer - GE07DE

We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.

Sr Staff AI Data Engineer is responsible for Implementing AI data pipelines that bring together structured, semi-structured and unstructured data to support AI and Agentic solutions. This Includes pre-processing with extraction, chunking, embedding and grounding strategies to get the data ready.

This role will have a Hybrid work schedule, with the expectation of working in an office location (Hartford, CT; Chicago, IL; Columbus, OH; and Charlotte, NC) 3 days a week (Tuesday through Thursday).

• AI Data Engineering lead responsible for Implementing AI data pipelines that bring together structured, semi-structured and unstructured data to support AI and Agentic solutions. This Includes pre-processing with extraction, chunking, embedding and grounding strategies to get the data ready.
• Develop AI-driven systems to improve data capabilities, ensuring compliance with industry’s best practices.
• Implement efficient Retrieval-Augmented Generation (RAG) architectures and integrate with enterprise data infrastructure.
• Collaborate with cross-functional teams to integrate solutions into operational processes and systems supporting various functions.
• Stay up to date with industry advancements in AI and apply modern technologies and methodologies to our systems.
• Design, build and maintain scalable and robust real-time data streaming pipelines using technologies such as Apache Kafka, AWS Kinesis, Spark streaming, or similar.
• Develop data domains and data products for various consumption archetypes including Reporting, Data Science, AI/ML, Analytics etc.
• Ensure the reliability, availability, and scalability of data pipelines and systems through effective monitoring, alerting, and incident management.
• Implement best practices in reliability engineering, including redundancy, fault tolerance, and disaster recovery strategies.
• Collaborate closely with Dev Ops and infrastructure teams to ensure seamless deployment, operation, and maintenance of data systems.
• Mentor junior team members and engage in communities of practice to deliver high-quality data and AI solutions while promoting best practices, standards, and adoption of reusable patterns.
• Develop graph database solutions for complex data relationships supporting AI systems.
• Apply AI solutions to insurance-specific data use cases and challenges.
• Partner with architects and stakeholders to influence and implement the vision of the AI and data pipelines while safeguarding the integrity and scalability of the environment.

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field.
• 8+ years of strong hands-on data engineering experience including Data solutions, SQL and No
  
  SQL, Snowflake, ETL/ELT tools, CICD, Bigdata, Cloud Technologies (AWS/Google/AZURE), Python/Spark, Datamesh, Datalake or Data Fabric.
• Strong programming skills in Python and familiarity with deep learning frameworks such as PyTorch or Tensor Flow.
• Experience in implementing data governance practices, including Data Quality, Lineage, Data Catalogue capture, holistically, strategically, and dynamically on a large-scale data platform.
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
• Strong written and verbal communication skills and ability to explain technical concepts to various stakeholders.

• Experience in multi cloud hybrid AI solutions.
• AI Certifications
• Experience in Employee Benefits industry
• Knowledge of natural language processing (NLP) and computer vision technologies.
• Contributions to open-source AI projects or research publications in the field of Generative AI.
• Experience with building AI pipelines that bring together structured, semi-structured and unstructured data. This includes pre-processing with extraction, chunking, embedding and grounding…