Non-internship professional experience in data engineering or a closely related discipline
Experience building and operating data pipelines (batch and/or streaming) using frameworks such as Spark, Airflow, dbt, or equivalent
Proficiency in Python and SQL
Experience with cloud-native data services including data warehouses, object storage, event streaming, and serverless compute
Familiarity with data modeling and data quality practices
Experience with software development life cycle practices including code reviews, source control, CI/CD, testing, and operational support
Demonstrated use of generative AI tools (e.g., agentic coding assistants, AI-powered IDEs) in a professional or project setting
Experience designing or building AI agents or multi-agent solutions that automate engineering workflows
Familiarity with agentic AI patterns including tool use, function calling, and multi-agent orchestration
Familiarity with at least one agentic AI development IDE
Experience building or maintaining shared data models, semantic layers, or data contracts
Familiarity with data governance, cataloging, or lineage tracking
Experience contributing to shared platform infrastructure, developer tooling, or self-service data services
Familiarity with observability tooling for data pipelines (logging, metrics, alerting)
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