Netflixs Localization Analytics Modernization combines audit, unified data structures, and event‑level insight to replace fragmented pipelines with a single source of truth.

Audit and Consolidation

The first step was a systematic review of existing dashboards and pipelines. By cataloguing usage patterns and code quality, the team identified overlap and low‑value assets.

• Reviewed >40 dashboards to classify relevance and maintenance cost.
• Removed duplicated SQL logic across isolated domains.
• Prioritized high‑impact visualizations for migration.
• Documented findings in an internal wiki for transparency.
• Established a governance checklist for future dashboard creation.

Unified Data Layer

Centralizing business rules into shared tables eliminates the need to repeat logic. The new Language Asset Producer table serves as the single source for who made this dub? queries.

• Created a normalized schema that captures asset provenance.
• Implemented write once, read many patterns using AWS Glue and Athena.
• Integrated versioned views to support downstream metrics without code changes.
• Enabled automated testing of data contracts via dbt.
• Linked to internal best‑practice guide GitHub sub‑issues workflow for change tracking.

Tool Revamp for Stakeholder Experience

Beyond code, the team rebuilt the Language Asset Consumption tool to reduce user friction. Combining audio and text languages into a single consumption metric clarifies original vs. localized usage.

• Unified dub and subtitle metrics into a composite consumption score.
• Added visual storytelling elements such as heatmaps for language preference.
• Provided self‑service filters to let analysts explore original vs. localized consumption.
• Implemented accessibility annotations following internal design‑system guidelines.
• Collected stakeholder feedback through quarterly surveys to guide iterative improvements.

Event‑Level Analytics Expansion

Moving from asset‑level to event‑level data enables granular insight into subtitle timing and reading speed. This feeds directly into style‑guide recommendations for linguists.

• Designed a generic timed‑text event schema stored in a partitioned Parquet lake.
• Captured line‑by‑line subtitle events with timestamps and user engagement flags.
• Connected the event model to real‑time dashboards via Amazon Kinesis Data Streams.
• Ran A/B tests to correlate reading speed with completion rates.
• Published findings in an internal knowledge base Scalable Data Platform guide.