Nithin Sriram

Driving the development of scalable data models and experimentation frameworks to support product growth, engagement, and monetization at Nextdoor. Partnered cross-functionally to define metrics, build pipelines, and deliver reliable data infrastructure powering self-serve analytics and strategic decision-making.

- Designed and developed data architecture for new sets of metrics, establishing a robust framework for tracking and analyzing previously non-existent performance indicators. - Utilized DBT for data transformations and modeling, ensuring efficient data processing and modeling best practices - Led migration of multiple schemas from legacy database, from source to reporting layer, with a focus on model optimization and scalability - Streamlined ETL processes, ensuring seamless data flow to enhance company-wide reporting and analytics - Implemented processes to standardize data engineering practices, reducing production issues by 5% and ensuring consistency across data warehouse

- Built data models in SQL to use for company-wide querying and reporting - Discovered and resolved gaps in data infrastructure and reporting to automate 30 man-hours a week and improve visibility and efficiency of Sales team - Utilized Looker and Tableau to present detailed data stories and recommend go-to-market business strategies to CXOs -Worked cross-functionally with Data Engineering, Product, and Customer Adoption teams to design dashboards and build data models to provide detailed business insight

- Drove pipeline opportunities by conducting customer targeting analyses, assisting sales teams with pipeline management, and improving workflows by restructuring the sales funnel - Built machine-learning algorithms and used predictive modeling in Python to determine which add-on products customers are likely to purchase - Analyzed revenue and sales reports using SQL to gain business intelligence, lead weekly sales forecasting calls, and optimize business processes - Optimized data models in SQL to improve querying efficiency of sales and strategy team

- Developed scalable forecasting and machine-learning models in Python to simulate business scenarios and provide data-driven recommendations, reducing client operating expenses by 16% - Analyzed forecasting models and created dashboards by visualizing data using Tableau

- Built pilot project from scratch revolving around Salesforce Marketing Cloud geared to improve customer acquisition and brand awareness, resulting in investment from executive board - Analyzed customer and competitor data to redefine target demographics and optimize design of customer journey, increasing click-through-rates by 27% - Identified KPIs and used SQL to query customer data and propose strategic initiatives to increase website traffic, conversion rates, and number of qualified leads