Audience & Marketing Insights Analyst

Responsibilities

• Build and maintain automated ELT pipelines that bring data from HubSpot, GA4, social, video, podcast, paid media, and event platforms into BigQuery through APIs and native integrations, replacing manual exports.
• Organize marketing and audience data in BigQuery using a medallion architecture, developing Dataform transformations that turn raw and semi-structured data (such as the GA4 event export and JSON API responses) into cleaned, reporting-ready tables.
• Document data models, shared identifiers, transformation rules, and metric definitions in a data dictionary so that marketing, audience, CRM, web, and content data connect reliably.
• Monitor pipelines and data freshness so that failures are caught before they affect reporting, and manage code in Git and GitHub so the work stays reproducible, maintainable, and transferable to others.
• Handle audience and contact data, credentials, and system access responsibly and in accordance with Stanford privacy and information security policies and applicable regulations.
• Ensure campaigns, content, and audience initiatives launch with appropriate tracking, tagging, and measurement plans, and maintain consistent campaign naming and UTM conventions.
• Define GA4 event and data layer specifications in partnership with web developers, and QA tracking implementations using tools such as Google Tag Manager preview mode and GA4 DebugView.
• Resolve tracking and data-quality issues with Hoover’s web team and external partners, and investigate discrepancies between GA4, HubSpot, and platform-reported figures before conclusions are presented to stakeholders.
• Characterize who Hoover is reaching (for example, by geography, profession, content interests, and relationship with Hoover) and how effectively it reaches its priority audiences, using CRM, web, platform, event, and survey data.
• Develop measurement frameworks, KPIs, and benchmarks for reach, resonance, and relationships that distinguish high-volume activity (passive impressions and views) from engaged reading, viewing, and listening.

Requirements

• Minimum Education: Bachelor’s degree.
• Minimum Experience: One year of experience or a combination of education and relevant experience. Experience in a quantitative discipline such as economics, finance, statistics, or engineering.
• Knowledge, Skills and Abilities: Basic knowledge and demonstrated experience using and applying analytical software, database management system software, database reporting software, database user interface and query software, and data mining software.
• Ability to collect data using a variety of methods, such as data mining and hardcopy or electronic documentation study, to improve or expand databases.
• Strong listening, verbal, and written communication skills.
• Ability to manage multiple activities in a deadline-oriented environment; highly organized, flexible, and rigorous attention to detail.
• Ability to use logic to calculate data; efficiently construct a database or scrutinize the form of a question.
• Ability to work with data of varying levels of quality and validity.
• Demonstrated ability to produce data in a clear and understandable manner meeting user requirements.
• Ability to work effectively with multiple internal and external customers.

Preferred

• Experience with cohort analysis, audience segmentation, funnel analysis, and audience journey analysis.
• Experience with attribution modeling, incrementality testing, or marketing mix modeling (e.g., Google’s Meridian or Meta’s Robyn).
• Experience with regression analysis, Bayesian or sequential testing methods, and causal inference techniques such as difference-in-differences or synthetic control.
• Experience with dbt, managed ELT connectors (e.g., Fivetran, Supermetrics, or Airbyte), Google Cloud services (e.g., Cloud Run, Cloud Scheduler, and Secret Manager), and CI/CD tools such as GitHub Actions.
• Experience using AI responsibly and in line with institutional policy, including BigQuery ML or natural language processing to classify unstructured text and AI coding assistants to accelerate development.
• Familiarity with web and UX analytics tools (e.g., Microsoft Clarity, Hotjar, Chartbeat, or Parse.ly), SEO tools (e.g., Semrush or Ahrefs), survey tools (e.g., Qualtrics or SurveyMonkey), and social listening tools (e.g., Sprout Social or Brandwatch).
• Experience in media, publishing, think tanks, nonprofits, higher education, or other mission-driven, content-focused organizations.