Tom is an experienced data modeler with a decade of experience in the technology sector. He's worked across the data stack in various roles, including data scientist, analytics engineer, and data engineer. His professional experience includes data roles at Facebook and Miro, where he led data warehousing, data modeling, and data platform initiatives.
Built and managed data infrastructure for a program's in-house CRM platform. Data infrastructure was used for sourcing new leads, monitoring program efficiency, and integrating data sources.
Managed analytics for a new, experimental product developed for small businesses, including targeting criteria, feature-flags used during release, and developing insights about product adoption.
Built and maintained lead-scoring models for an on-platform campaign that drove a 30% improvement in cost per campaign objective.
Implemented Airflow and constructed the first data pipelines for the company.
Managed the building and design of a data warehouse, including integrating data sources.
Built a lead scoring system that automatically funneled leads to the sales team. The system included data enrichment, scoring, and result tracking.
Built data pipelines and a scoring system to measure and track the prevalence of account sharing for a video streaming service.
Created revenue and operations tracking dashboards for a sales channel of a US wireless carrier.
Developed cost-allocation models to assess the profitability of individual enterprise customers for a US communications service provider.
Greenfield Docker-based Airflow deployment was created for building the company's first data pipelines that connected internal services with their data warehouse.
The Airflow app computed KPIs such as daily active users and managed lead-scoring of new users while also monitoring data ingestion quality.
The data pipelines were crucial for projects like A/B testing pricing models and validating effects of new feature releases on customer-facing apps.
Developed an automated lead scoring system ranking new users based on their likelihood to become qualified leads.
The system utilized data from enhancement sites like Clearbit, user engagement with marketing content, and product usage to allocate qualified leads to the sales team.
This lead scoring system aimed at increasing the efficiency of the inside sales team and the effectiveness of lead nurturing campaigns.
Built data analytics system including pipelines and statistical calculations for A/B tests on company's pricing and feature releases.
Provided significant strategy recommendations from the analytics results.
Significant impact made on the company's pricing strategies and product roadmap.
