Toptal is a global remote company providing a freelancing platform that connects top-tier talent with businesses worldwide. As a Senior Analytics Engineer, I develop and maintain the scalable data infrastructure that supports Toptal's data-driven decision-making processes.𝗞𝗲𝘆 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗻𝗱 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Pioneered the development of a new single-source-of-truth data warehouse, implementing star schema models for efficient data organization and galaxy schemas for complex data structures.𝗘𝗧𝗟 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Constructed and maintained robust ETL pipelines using SQL, BigQuery, and Dataform to improve scalability and performance. Successfully migrated legacy Dataform application to Google Cloud Platform (GCP). Translated outdated ETL processes from Python to SQL.𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗧𝗼𝗼𝗹𝘀: Implemented sophisticated alerting, notification, and security systems integrated with Slack for real-time data quality and privacy monitoring using Python, GitHub CI/CD, Docker, and Jenkins. Developed the backend services for internal Streamlit data applications using Python, GitHub CI/CD, and Google App Engine.𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: Developed a comprehensive data dictionary catalog, defining critical fields across 300+ business-essential tables.

Sourceress was a 2017 Y Combinator-backed startup that developed ML to revolutionize hiring processes. We achieved Series A funding and served clients like Airbnb, Cruise Automation, Ginkgo Bioworks, Medium, and Plaid before operations ceased in Jan 2020. As Lead Machine Learning Data Analyst, I played a crucial role in developing and refining our ML-driven hiring solutions.𝗞𝗲𝘆 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗻𝗱 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀:𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗻𝗱 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: Created foundational heuristics for our ML models, laying the groundwork for our AI-driven candidate matching system. Developed more intelligent, targeted searches for our clients using matured models, significantly improving match quality and efficiency.𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Analyzed and queried our candidate database to create insightful data visualizations with Periscope. Developed automated metrics tables using SQL, enhancing our ability to track and report on key performance indicators.𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗠𝗼𝗱𝗲𝗹 𝗜𝗻𝘁𝗲𝗴𝗿𝗶𝘁𝘆: Implemented Root Mean Square Error (RMSE) tracking methods to monitor and improve the accuracy of our ML models. This initiative significantly enhanced our model performance and reliability.𝗧𝗲𝗮𝗺 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽: Led a team of analysts in our Machine Learning and Operations departments, fostering a collaborative environment and driving innovation in our data-driven approaches to sourcing candidates.