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Metro location and popular Data Engineer title increase applicant density, while seniority narrows the pool.
Core data engineering and AI agent skills transfer across industries but require significant technical depth.
Explicit 7+ years plus mandatory dbt, Snowflake/Redshift, Airflow, Python, and LLM skills create strict filters.
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Lead design and implementation of end-to-end data automation workflows including ingestion, transformation, validation, and distribution with minimal human intervention.
Architect and integrate cloud-based data solutions (Data Lake, Data Warehouse) supporting analytics, machine learning, and AI-powered self-service.
Drive adoption of Large Language Model (LLM) augmented capabilities such as RAG pipelines, semantic search, and AI-assisted data products inside the data platform.
7+ years professional experience as a Senior Data Engineer with hybrid Data Lake and Data Warehouse expertise.
Proficient in cloud analytical warehouses (Redshift, Snowflake), data transformation with dbt, and workflow orchestration using Apache Airflow.
Advanced skills in Python and SQL programming.
Bachelor's or Master's degree in Computer Science or similar discipline.
Experienced in architecting scalable data platforms with automation in hybrid cloud environments using AWS and modern orchestration tools.
Demonstrated ability to implement agentic workflows leveraging LLMs for data quality, validation, and coding assistance.
Strong foundation in engineering best practices including CI/CD, testing, agile methods, version control, and distributed systems knowledge.