Senior Staff AI Data Engineer - Hybrid

Primary Job Responsibilities

• Lead the implementation of AI data pipelines integrating structured, semi-structured, and unstructured data to support AI and agentic solutions, including preprocessing techniques such as extraction, chunking, embedding, and grounding (e.g., RAG, retrieval frameworks)
• Develop AI-driven data systems that enhance data capabilities while ensuring adherence to industry best practices
• Implement and optimize Retrieval-Augmented Generation (RAG) architectures and integrate them with enterprise data platforms
• Design, build, and optimize scalable batch and streaming data pipelines with a focus on performance, resiliency, and operational efficiency
• Develop and maintain real-time data streaming pipelines using technologies such as Snowpipe
• Develop data domains and data products to support reporting, analytics, AI/ML, and data science use cases
• Ensure the reliability, availability, and scalability of data pipelines through monitoring, alerting, and incident management
• Implement reliability engineering best practices, including fault tolerance, redundancy, and disaster recovery
• Drive engineering discipline across data platforms, including observability, data quality, lineage, and governance
• Collaborate with DevOps and infrastructure teams to enable seamless deployment and operation of data systems
• Partner with cross-functional teams to integrate data and AI solutions into business processes and enterprise systems
• Provide architectural leadership in partnership with Data Architects, including defining technical standards and influencing enterprise-wide practices
• Develop and integrate graph database solutions to support complex data relationships within AI systems
• Apply GenAI approaches to insurance-specific data use cases and challenges
• Lead the development of AI-ready data foundations that support scalable, production-grade solutions
• Ensure data platforms remain resilient, governed, and cost-efficient, aligned with enterprise cloud and data strategies
• Mentor junior engineers and contribute to communities of practice, promoting best practices, reusable patterns, and engineering standards
• Stay current with advancements in GenAI and apply relevant technologies and methodologies to platform evolution

Skills

• Strong technical expertise in AI-driven data solutions leveraging modern cloud platforms
• Deep expertise in core data engineering, including advanced SQL, data modeling, and query performance tuning
• Strong experience in ETL/ELT architecture, orchestration frameworks, and pipeline optimization
• Experience working across teams with strong communication and stakeholder management skills
• Proven ability to mentor and develop AI and data engineering talent
• Knowledge of emerging AI and data engineering design patterns
• Strong planning, organization, and execution capabilities
• Ability to lead in a lean, agile, and fast-paced environment, leveraging Scaled Agile practices
• Strong analytical and problem-solving skills with the ability to translate business requirements into technical solutions
• Demonstrated leadership capability to own architecture decisions and drive cross-team alignment
• Effective collaboration, decision-making, and relationship-building skills
• Strong interpersonal skills with the ability to provide thought leadership in a dynamic environment

Qualifications

• Candidates must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.
• Bachelor’s degree in Computer Science, Artificial Intelligence, or a related field
• 8+ years of data engineering experience with deep expertise in SQL, data modeling, and large-scale data processing systems
• Proven experience designing and optimizing ETL/ELT pipelines and orchestration frameworks in enterprise environments
• Experience supporting Generative AI data engineering use cases
• Hands-on experience implementing production-ready, enterprise-grade GenAI data solutions
• Experience implementing RAG pipelines, including retrieval, chunking, embedding, and grounding techniques
• Experience operationalizing GenAI pipelines in production environments
• Hands-on experience with cloud ecosystems (AWS, GCP, Azure, Snowflake) and Python-based data engineering stacks
• Proven ability to deliver resilient, governed, and cost-efficient data platforms at scale
• Experience with vector databases and graph databases, including design and optimization
• Experience working with unstructured data for GenAI applications
• Experience implementing data governance practices, including data quality, lineage, and data cataloging at scale
• Proficiency in building AI data pipelines that integrate structured and unstructured data with preprocessing techniques
• Strong programming skills in Python
• Strong communication skills and ability to explain technical concepts to a broad set of stakeholders