• Design, build, and maintain data pipelines using Snowflake, Airflow, and DBT • Lead architectural discussions around the modern data stack • Develop scalable ETL and ELT processes using Python and SQL • Build and enforce data quality frameworks across the platform • Integrate internal and external systems via REST APIs • Partner with data science and product teams to prototype and launch data products • Contribute to code reviews and mentor teammates • Leverage AI coding tools while maintaining high quality and security standards

• Take technical ownership of the team’s data stack, including implementation of tools for tracking, reporting, and automation • Implement and maintain integrations such as tracking pixels, server-to-server (S2S) connections, and event tracking systems to support marketing campaigns • Build and maintain pipelines for real-time reporting on spend and performance across platforms • Design and implement attribution models to measure and improve campaign effectiveness • Act as the technical point of contact (POC) for the User Acquisition team, troubleshooting issues related to tracking, naming conventions, URLs, and APIs — including coordination with external partners • Develop and implement automation solutions to improve the User Acquisition team’s productivity • Collaborate with cross-functional teams to support marketing initiatives and resolve technical challenges • Build pipelines for inventory forecasting across DTC (Shopify) and marketplaces (Amazon, TikTok Shop, etc.), supporting Operations and Logistics teams in marketing-aligned planning

• Design, implement, and maintain scalable data pipelines and ETL/ELT workflows on Databricks and cloud platforms. • Manage large-scale geospatial and temporal datasets stored in AWS S3. • Collaborate with data scientists to productionize machine learning models and ensure smooth data availability. • Implement data validation, testing, and monitoring frameworks to ensure data accuracy, consistency, and reliability. • Optimize data storage and processing strategies to handle high volumes of traffic and mobility data efficiently. • Develop and maintain documentation for data workflows, architecture, and processes. • Work closely with cross-functional teams to understand data requirements and ensure timely delivery. • Stay up-to-date with the latest trends and best practices in data engineering, cloud technologies, and big data processing.

• Translate marketing analytics and survey data requirements into scalable data models, automated pipelines, and reporting solutions • Design and develop data warehouses, dashboards, and analytical frameworks to support marketing programs and campaign performance measurement • Build and optimize ETL processes integrating structured and unstructured data sources • Solve complex data integration challenges using advanced SQL techniques and data engineering best practices • Collaborate with data engineers, data scientists, and marketers to define data requirements and present insights clearly and effectively • Conceptualize and evaluate large-scale data architectures, balancing performance, scalability, and operational efficiency • Implement and maintain data security and privacy safeguards, ensuring compliance and resolving data quality issues