Responsibilities

• Build and maintain automated reporting pipelines, dashboards, and datasets that support Lifecycle Marketing performance
• Improve LCM data foundations by implementing standardized logic, definitions, and quality checks across communications channels (e.g., email, push, in-app, and related CRM activity).
• Develop and enhance self-serve reporting and AI-assisted analytics tooling so LCM stakeholders can monitor performance and answer routine questions independently.
• Investigate and help resolve data discrepancies, delays, or pipeline issues affecting LCM reporting and campaign measurement.
• Partner with Engineering and Marketing Data Science to productionize data products, automate recurring workflows, and improve reliability of LCM datasets.
• Support time-sensitive stakeholder requests by extending existing reporting frameworks rather than creating one-off, unmaintainable analyses.
• Contribute to documentation of data definitions, business logic, and reporting processes so LCM leads and MDS partners can work from a shared source of truth.
• Support incrementally measurement for lifecycle campaigns, including holdout and experiment design, analysis, and translating lift results into recommendations for stakeholders.
• Collaborate with LCM, Product, Engineering, and MDS partners to gather requirements, validate outputs, and translate findings into clear, actionable insights.

Requirements

• Bachelor’s degree in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative field.
• 2+ years of experience in data science, analytics, or data engineering roles supporting marketing, lifecycle/CRM, communications, growth, or product analytics.
• Strong proficiency in SQL and working knowledge of Python for data analysis and pipeline development.
• Experience building or maintaining dashboards and self-serve reporting solutions (e.g., Tableau or similar BI tools); interest in AI-assisted self-serve analytics is a plus.
• Familiarity with modern data stacks and workflow tools such as dbt, Airflow, and cloud data warehouses (e.g., Snowflake), or a strong willingness to learn.
• Experience working with structured datasets and applying consistent business logic across reporting outputs; familiarity with marketing communications or CRM data is a plus.
• Experience with incrementality measurement (e.g., holdouts, A/B tests, or other causal methods) is preferred.
• Ability to follow established standards while contributing thoughtful improvements to data quality, efficiency, and maintainability.
• Strong attention to detail and comfort working with operational data that requires high accuracy.
• Clear communication skills and the ability to collaborate effectively with cross-functional, non-technical partners.