How to Become a AI Data Lineage Analyst

Build a simple ML pipeline (data ingestion → feature engineering → model training → evaluation) using Airflow and scikit-learn, then implement OpenLineage integration to automatically capture and visualize lineage in Marquez. Produce a lineage diagram showing every dataset, transformation, and model artifact.

Create a dbt project with 30+ models across staging, intermediate, and mart layers. Ingest the dbt project into LinkedIn DataHub and configure column-level lineage tracking. Write a Python script that queries the DataHub API to generate a column-level lineage report for any given mart table.

Build a LangChain RAG pipeline over a set of PDF documents using ChromaDB. Implement custom LangChain callbacks that log every retrieval event (which chunks were retrieved, similarity scores, source documents) and store provenance metadata in a PostgreSQL lineage database. Create a dashboard that traces any chatbot response back to its source documents.

Design a Neo4j graph database schema for data lineage. Build an ETL pipeline that extracts metadata from dbt and Airflow into the graph. Write Cypher queries that support GDPR use cases: tracing all storage locations for a specific data subject's PII, identifying all models trained on PII data, and generating deletion propagation reports.

Build a service that monitors upstream data source changes (schema modifications, volume anomalies) and automatically traverses a lineage graph to compute blast radius - identifying all downstream ML models, dashboards, and reports affected. Integrate with Slack for automated alerting with severity scoring based on downstream criticality.

Build a tool that automatically generates model cards (per Google's Model Cards Toolkit) by extracting lineage metadata from MLflow and OpenLineage. The model card should include: training data provenance, feature lineage, data quality metrics at training time, and known limitations derived from lineage gap analysis.