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Popular mid-level data title in a metro market but niche LLM/AI requirements moderate applicant density.
Specialized data engineering and production LLM expertise moderately limits cross-industry transferability.
Mandatory 5–8 years plus production LLM, pipeline, and warehouse experience enforces strict shortlisting.
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Own and scale large-scale data ingestion pipelines handling millions of records from web and third-party sources, ensuring reliability and efficiency.
Build and maintain AI-powered data systems using LLMs and embeddings for tasks such as extraction, classification, entity resolution, and enrichment to improve data quality and structure.
Design and manage data models, enrichment layers, and synchronization between warehouse, CRM, and products with responsibility for AI infrastructure cost and performance optimization.
5-8 years of data engineering experience with end-to-end ownership of data systems.
Strong proficiency in SQL and Python with hands-on experience in modern cloud data warehouses.
Production experience in building ingestion/ETL pipelines at scale and AI fluency, including developing production systems using LLMs, embeddings, vector search, and cost-effective model deployment.
Experience in data matching, enrichment, or entity resolution with a strong understanding of cloud infrastructure and cost/performance trade-offs.
Experienced data engineer comfortable independently solving ambiguous problems and mentoring others within fast-moving, high-ownership environments.
Deep AI expertise integrated into data engineering work, with a clear perspective on when and how to apply models versus traditional methods.
Strong strategic focus on building scalable, reliable, and cost-effective AI-enhanced data infrastructure critical to the company’s core product offering.