Data lineage is vital for data engineers as it traces the journey of data from its origin to its destination, aiding in troubleshooting, regulatory compliance, and understanding the impact of upstream changes on downstream data. The text describes a demo project that utilizes dlt and dbt to establish data lineage, focusing on a skate shop's sales data from Shopify and physical stores, which is then loaded into BigQuery. The demo illustrates creating table, row, and column lineage using dlt's load_info feature, which captures schema changes during data ingestion. The process includes using dbt to transform raw data into a fact_sales table for analytical purposes, with lineage details visualized through a dashboard in Metabase. This approach enables tracking of data changes and lineage at multiple levels, providing valuable insights for maintaining robust data pipelines.