Create scripts, CLI tools, and UIs (e.g., Streamlit) for data quality.
Query large datasets to surface insights, find data quality issues, inform actions.
Collaborate with engineers to build backend tools and data pipelines.
Translate analyst pain points into technical solutions to reduce manual effort.
Develop automated checks and monitoring to flag data anomalies.
Rapidly test new approaches to data quality and scale successful solutions.

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