6+ years of experience in data engineering, with at least 2+ years working on data infrastructure for AI/ML systems;
Expert-level Python skills and strong SQL proficiency across multiple database engines;
Production experience with modern data stack: dbt, Spark (PySpark), Airflow/Prefect/Dagster, and cloud data warehouses (Snowflake, Databricks, BigQuery);
Hands-on experience with vector databases (Pinecone, Weaviate, ChromaDB, pgvector) and building RAG data pipelines;
Experience building data pipelines on at least one major cloud platform: AWS (S3, Glue, Redshift, EMR), Azure (ADLS, Synapse, Data Factory), or GCP (BigQuery, Dataflow, Dataproc);
Strong understanding of data modeling: dimensional modeling (Kimball), data vault, and modern analytical modeling patterns;
Experience with data quality frameworks and tools: Great Expectations, Soda, dbt tests, or equivalent;