Data Lineage Engineer
Data Lineage Engineers build and maintain the systems that track how data flows across pipelines, transformations, and systems. Their daily English covers writing lineage design documents, presenting impact analysis findings to stakeholders, explaining lineage concepts to analysts and product teams, and producing data governance reports. This path covers the vocabulary of data observability, governance, and the language needed to communicate data trust across an organisation.
Topics covered
- OpenLineage & standards
- Column-level lineage
- Impact analysis
- Data catalogues
- Data governance
- Lineage visualisation
Vocabulary spotlight
4 terms every Data Lineage Engineer should know in English:
A directed graph representation of how data assets are connected through transformations — nodes are datasets or columns, edges represent data flow or derivation
"The lineage graph revealed that 14 downstream dashboards would be affected by the schema change in the orders table."
Fine-grained lineage tracking at the individual column level — showing exactly which source columns feed into each output column, enabling precise impact analysis
"Column-level lineage showed that the revenue metric derived from three source columns across two tables — critical for the audit trail."
An open standard API specification for capturing and exchanging data lineage information across tools and platforms — enabling interoperable lineage collection
"We instrumented our Airflow pipelines with OpenLineage emitters so lineage flows automatically to our Marquez lineage backend."
The process of using lineage data to determine which downstream assets would be affected by a change to an upstream dataset or column
"Impact analysis before the schema migration identified 23 affected queries — we scheduled coordinated updates to avoid breaking production reports."
📚 Vocabulary Reference
Key terms organised by category for Data Lineage Engineers:
Lineage Concepts
Standards & Tooling
Governance
Communication
Recommended exercises
Real-world scenarios you'll practise
- Presenting a lineage platform proposal to a data governance committee: explaining the business value of automated lineage tracking for compliance and debugging
- Writing an impact analysis report before a major schema migration: listing affected downstream assets, risk levels, and required coordination
- Explaining column-level lineage to a compliance officer: showing how a specific PII column flows from source to reporting layer
- Onboarding analysts to the data catalogue: teaching them to navigate lineage graphs and interpret upstream/downstream relationships