Responsibilities

• Build, validate, and operate audience segmentation and activation workflows in Salesforce Marketing Cloud across lifecycle programs.
• Work directly with large-scale behavioral, lifecycle, transactional, engagement, and event data in Snowflake to identify meaningful audience patterns and opportunities.
• Help shape the source eventing and data inputs needed to answer important customer and business questions, partnering closely with Product, BI, and Engineering.
• Design rigorous experiments, analyze results, and use what we learn to continuously improve audience strategy, lifecycle experiences, and personalization.
• Build clear reporting and communicate findings to leaders so evidence can move quickly into decisions, operational rules, and better product experiences.

Requirements

• 5+ years of hands-on Salesforce Marketing Cloud experience supporting production lifecycle programs, including audience segmentation and activation.
• Strong experience in lifecycle marketing analytics, audience segmentation, and marketing automation operations.
• Meaningful experience in consumer marketing or consumer product environments, working with individual customer behavior and lifecycle signals at scale.
• Advanced SQL skills and strong hands-on Snowflake experience working directly with large customer datasets.
• Strong analytical and reporting capability, including the ability to investigate ambiguous problems, validate data, interpret results, and communicate findings clearly to business and technical leaders.
• Hands-on experience designing and evaluating experiments, including incrementality, statistical testing, or other rigorous measurement approaches.
• Curious, driven, and energized by solving ambiguous, high-impact problems with smart, collaborative people.

Preferred

• Experience with event instrumentation, data modeling, schema design, pipelines, orchestration, identity resolution, or data quality frameworks.
• Python experience for analysis, automation, or repeatable data workflows.
• Exposure to applied data science or machine learning approaches such as clustering, propensity modeling, classification, or recommendation systems.
• Experience with Salesforce Personalization, mobile push, SMS, or other lifecycle activation capabilities.
• Exposure to GA4, dbt, Airflow, Fivetran, or similar analytics and data engineering tools.
• Curiosity and a desire to grow deeper into data engineering and applied data science while remaining close to the operational decisions and customer experiences the work enables.