• Design, build, and maintain data pipelines that ingest and process structured and unstructured data sources such as surveys, support tickets, call transcripts, and product usage data. • Develop and experiment with scalable data processing workflows that support downstream analytics, machine learning, and large language model (LLM) use cases. • Transform, validate, and model large, multi-dimensional customer behavior and usage datasets to ensure they are reliable, well-structured, and analytics-ready. • Partner with data scientists, analysts, and business stakeholders to enable clear understanding and effective use of data through well-defined datasets, documentation, and data quality standards. • Document data pipelines, schemas, and engineering best practices, and share learnings within the team to help promote a strong, data-driven culture at Dropbox. • Collaborate proactively with stakeholders across Customer Experience and Success to understand business needs, translate requirements into technical data solutions, and support accurate and timely data delivery.

• Apply natural language processing (NLP) techniques to identify trends and patterns in unstructured text data such as surveys, support tickets, and call transcripts. • Develop and experiment with solutions using large language models (LLMs) to help automate and scale analytical processes. • Use machine learning and statistical methods to extract actionable insights from large, multi-dimensional customer behavior and usage datasets. • Communicate complex analytical concepts to non-technical audiences through clear written and verbal communication, supported by compelling data visualizations. • Document, gather, and share best practices and insights within the team to help promote a data-driven culture at Dropbox. • Collaborate proactively with stakeholders across Customer Experience and Success to understand business needs and support accurate and relevant analytical deliverables.

• Develop and maintain scalable data pipelines for diverse client datasets • Collaborate cross-functionally to translate complex problems into data-driven solutions • Design, implement, and optimize machine learning models for product integration • Rapidly prototype and iterate on novel analytics approaches with a focus on real-world impact • Participate in the end-to-end product development lifecycle, from conception through integration

• CDP Ownership: Take charge of the CDP applications, ensuring they are efficient to support customer-centric strategies and objectives across our brands. • Marketing Technology Guidance and Collaboration: Provide technology guidance to the consumers of our customer data as well as brand partners. (examples: Loyalty, ESP, CRM) • Team Leadership: Lead and motivate a team focused on leveraging customer data to enhance cross brand marketing, drive initiatives aimed at improving the overall customer experience and revenue. • Data Utilization: Ensure that the right customer data is accessible and utilized effectively for targeted marketing campaigns, personalized communications, and loyalty program enhancements. • Data Management: Oversee the management and maintenance of customer data within the CDP application, ensuring accuracy, completeness, and security. • Snowflake & Cloud Expertise: Leverage advanced knowledge of Snowflake architecture and cloud technologies (Azure, AWS, GCP) to optimize data storage, migration, and integration strategies. • Vendor Relationship Management: Manage relationships with third-party vendors, ensuring alignment with business objectives and compliance standards. • Compliance & Governance: Review Statements of Work (SOW) and ensure adherence to compliance and governance frameworks. • Agile & Project Management: Apply Agile methodologies and JIRA administration skills to manage project workflows, sprint planning, and delivery timelines. • Campaign Optimization: Collaborate with marketing teams to optimize CDP campaigns, ensuring personalized and targeted communications to drive customer retention and acquisition. • Collaboration: Collaborate closely with marketing, analytics, and technology teams to align customer data strategies with broader business objectives and ensure seamless integration with marketing initiatives. • Performance Monitoring: Monitor and analyze the performance of CDP activities and customer engagement metrics, providing insights and recommendations for continuous improvement. • Technology Integration: Work closely with technology teams to integrate and optimize CDP systems, ensuring seamless functionality and alignment with business needs.