逗号简历

Designed the data models in Microsoft Azure Kusto Systems for tracking and ingesting product telemetry data to Snowflake data warehouse, thus making 15+ feature usage data available at an hourly refresh rate with billions of data points ready for analysis/modeling for all data science use cases.

Led and executed on entire DBT strategy of automating, monitoring, and maintaining datasets for internal data mart thereby freeing the data engineering team's resources up to 50%.

Collaborated with product software engineers, data scientists, and product managers for governance around establishing standards for product instrumentation data.

Coordinated and streamlined process between upstream dependencies thereby reducing the time taken to land data in the analytic database from 6 weeks to 2 weeks.

Established TDD for internal data governance project to check for permissions, violations, and managing environments for data.

Designed, developed, and integrated python audit framework and monitoring system for increased visibility around 100% of automated data pipelines which helped the data engineering team meet all SLAs in terms of data availability and data quality.

Created automated data pipelines which scanned git repositories and landed data in Snowflake warehouse via AWS S3 which enabled product engineering teams to get access to internal engineering metrics like codebase transition, thereby reducing the internal code-migration roadmap timeline by 40%.

Delivered in-product feedback dashboard which ties feedback data to internal accounts data that enabled data scientists to perform Net Promoter Score Analysis, Sentiment Analysis and is currently used by 20+ teams with Docusign including PMs, Engineers, leadership.