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Metro location, popular data engineering title, broad skills, and recognizable brand increase applicant competition.
Core cloud and data engineering skills transfer across industries, though insurance domain knowledge is somewhat preferred.
Many mandatory technical stacks and senior architecture responsibilities impose strict screening filters.
Lead design and implementation of scalable, real-time data streaming and AI data pipelines integrating structured, semi-structured, and unstructured data for AI and agentic solutions.
Develop and manage data domains, products, knowledge graphs, and semantic layers supporting AI/ML and analytics, ensuring governance, reliability, and operational excellence.
Drive AI-driven data engineering innovations such as Retrieval-Augmented Generation (RAG) pipelines, synthetic data generation, and productivity improvements including automated data quality and DevOps practices.
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or related field.
Strong experience in data engineering with expertise in data architecture, cloud platforms (AWS/GCP/Azure), Snowflake, ETL/ELT, Python/Spark, and data governance.
Hands-on experience building production-grade GenAI data solutions including AI pipelines, semantic modeling, RAG implementation, and prompt engineering techniques.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced leader in complex data ecosystems who can influence AI and data pipeline strategy while ensuring scalability and integrity.
Deep domain expertise in building AI-driven data infrastructures integrating multiple data types and supporting advanced AI applications such as agentic solutions.
Proven ability to mentor junior engineers and collaborate effectively across teams in fast-paced, agile environments deploying modern cloud and AI technologies.