What role does data lineage play in your current project?

Data lineage is fundamental in my current project, serving as a critical tool for understanding data flow, ensuring data quality, and maintaining trust across our data ecosystem. It provides a historical record and visual map of data's journey from source to consumption.

Why Data Lineage is Crucial

Lineage plays several vital roles:

* Troubleshooting and Root Cause Analysis: When data quality issues or errors arise in a downstream report or application, lineage allows us to quickly trace the data back through various transformations (e.g., dbt models) and sources. This helps pinpoint the exact stage—be it an ingestion pipeline error, a faulty transformation logic, or an upstream source data problem—where the issue originated.
* Impact Analysis: Before implementing schema changes, deprecating a table, or modifying a transformation, lineage is indispensable. It reveals all dependent downstream assets (dashboards, ML models, other data products) that might be affected, enabling thorough planning and preventing unintended breaks.
* Compliance and Governance: For regulations like GDPR or CCPA, lineage helps track sensitive data (PII) from its point of ingestion, through all transformations, to its final resting place. This demonstrates data minimization, access control, and auditability, proving compliance. Snowflake's Access History feature, for instance, can provide insights into data usage and origin.
* Building Trust and Data Understanding: For data analysts and scientists, lineage provides transparency into data origins and transformations. It answers "where does this data come from?" and "how was it processed?", fostering confidence in the data and accelerating onboarding for new team members.

Implementation and Example

In our project, we primarily leverage dbt docs for generating and visualizing lineage graphs among our analytical models. This allows us to see dependencies between models defined using ref() statements.

-- dbt model: dim_customers.sql
SELECT
    c.customer_id,
    c.first_name,
    c.last_name,
    o.total_orders
FROM {{ ref('stg_customers') }} c
LEFT JOIN {{ ref('agg_customer_orders') }} o ON c.customer_id = o.customer_id

This simple SELECT statement implicitly defines lineage: dim_customers depends on stg_customers and agg_customer_orders. Beyond dbt, we also utilize metadata from our data warehouse (e.g., Snowflake's INFORMATION_SCHEMA or ACCESS_HISTORY views) to understand table-level dependencies and query patterns. For complex Spark transformations, tools like Spark Spline can provide similar insights into data flow.

In the interview, also mention…

Discuss how you would handle lineage for non-SQL transformations (e.g., custom Python scripts, streaming jobs) using custom metadata collection or specialized tools.