[Data Engineering] Deployed Airflow to programmatically author, schedule and manage workflows including data pipelines and ML models. Defined DAGs using python. Reports powered by airflow : metric aggregations, retention charts, marketing attribution models, investor dashboards, etc. [Data Engineering] built and maintained key canonical datasets and materialized views which powered ad-hoc analysis [Data Engineering] automated recurring analytical tasks (weekly/monthly/daily) using Apache Airflow. Following operations were deployed : executing SQL, running bash scripts, send notifications/alerts to slack channels, etc [Data Science] built retention model for multiple products allowing us to surface if any previous bugs or fixes improved the retention [Data Science] Worked collaboratively with SEO/ digital marketing team to run ads-retention analysis to see which paid ads channel (google/facebook) were performing better in terms of locally defined retention. To do this we built a native solution to filter traffic based on query parameters allowing us to visualize the complete funnel. This allowed the marketing team to have a weekly sync and rapidly improve the efficiency of the paid ads besides google and facebook's own definitions. [Data Science] Owned key reports across the company including : retention charts, aha-moment-analysis, user-session-analysis, churn prediction, acquisition funnel (AARRR) , retention funnel [Growth Marketing] Built a scalable email marketing framework to send personalized campaigns and attribute success based on user sessions / time spent / engagement based goals. [Growth Marketing]Built email marketing campaign to send emails to power users (using Intercom) to derive "mirror audiences" allowing hyper targeting as opposed to Facebook's internal mirror audience mechanism which was recently obfuscated the ability to hyper target facebook users which allowed us to work on this project.