Marketing Data Analyst

Stanford's Hoover Institution is seeking an Audience & Marketing Insights Analyst to build the analytics foundation behind smarter marketing decisions. In this role, you will design marketing data pipelines and models, then use the data to answer questions about reach, resonance, relationships, and what should change next. Success is measured by the quality of decisions your insights enable. What you'll do Build and maintain automated ELT pipelines that move data from HubSpot, GA4, social, video, podcast, paid media, and event platforms into BigQuery using APIs and native integrations, reducing manual exports. Organize marketing and audience data in BigQuery using a medallion architecture , and use Dataform transformations to convert raw and semi-structured inputs (including GA4 event export and JSON API responses) into cleaned, reporting-ready tables. Create and maintain a data dictionary documenting data models, shared identifiers, transformation rules, and metric definitions so marketing, audience, CRM, web, and content data connect reliably. Monitor pipeline health and data freshness , ensuring failures are caught before they impact reporting, and manage code with Git and GitHub for reproducibility and collaboration. Handle audience and contact data , credentials, and access responsibly and in line with Stanford privacy and information security policies and applicable regulations. Ensure campaigns launch with proper tracking, tagging, and measurement plans, including consistent campaign naming and UTM conventions . Define GA4 event and data layer specifications in partnership with web developers, then QA tracking using tools such as Google Tag Manager preview mode and GA4 DebugView . Troubleshoot tracking and data-quality issues with Hoover's web team and external partners, and investigate discrepancies between GA4, HubSpot, and platform-reported figures. Characterize audience reach using CRM, web, platform, event, and survey data, including geography, profession, content interests, and relationship with Hoover. Develop measurement frameworks for reach, resonance, and relationships , separating high-volume activity from engaged reading, viewing, and listening. Analyze cross-channel journeys from social media, search, paid media, video, podcasts, and earned exposure into Hoover-owned channels (Hoover.org, email, and events). Build audience cohorts and segments based on acquisition source, interests, behavior, and relationship stage. Evaluate campaigns through pre-, during-, and post-campaign analyses aligned to defined objectives, and identify what content, topics, fellows, formats, and campaigns drive acquisition and repeat engagement. Measure incremental impact of paid promotion using holdout groups, geographic tests, or platform lift studies where feasible, and distinguish paid, organic, earned, and direct activity. Analyze marketing costs and outcomes (for example, cost per engaged visitor , new subscriber, and event registrant) to support evidence-based budget allocation. Partner with marketing leadership to set objectives and measurement plans before major initiatives launch, and design A/B and multivariate tests across email, web, landing pages, paid media, messaging, and creative. Translate analysis into clear, actionable recommendations that influence decisions about content prioritization, distribution, audience acquisition, and investment. Build automated cross-channel dashboards (for example, Looker Studio on BigQuery reporting-ready tables) to support self-service visibility for content producers and program teams. Provide regular reporting and executive-level analysis highlighting metrics plus key findings, implications, and recommended actions for nontechnical audiences. Proactively identify patterns, opportunities, and risks in the data, and follow up to assess whether changes improved outcomes. Requirements Bachelor's degree in a relevant field and 2 years of relevant experience, or a combination of education and relevant experience solving analytical problems using quantitative approaches. Experience in marketing analytics, audience analytics, or digital analytics , working directly with marketing, communications, or content teams. Demonstrated experience combining data from multiple platforms and using it to shape marketing, content, or audience decisions. Ability to communicate analytical findings to nontechnical stakeholders . Technical and analytics skills GA4 (advanced): event-based data model, custom events/parameters/dimensions, key events, Explorations, and correct interpretation of reporting impacts from attribution settings, consent mode, and data thresholding. Data layer & Tag Management: Google Tag Manager, writing tracking specifications, and debugging implementations. HubSpot: pulling and analyzing contacts, custom objects, properties, lifecycle stages, lists, marketing email, forms, and subscriptions, including awareness of limitations such as Apple Mail Privacy Protection effects on open rates. Campaign tracking: UTM tracking, campaign taxonomy, Google Search Console, and native platform analytics (YouTube Studio, Meta Business Suite, LinkedIn, Google Ads) with understanding of metric definitions like views, reach, and engagement. BigQuery & Dataform: storing, querying, and transforming data (including nested/repeated fields), Dataform workflows with dependencies and data-quality assertions, and cost-aware querying. SQL: joins, window functions, common table expressions, plus JSON, regular-expression, and text functions. Python and JavaScript: data processing, automation, API calls, cleaning, statistical analysis, and GTM-related scripting (including Google Apps Script or Dataform as applicable). ELT and APIs: REST API data retrieval (authentication, pagination, rate limits) and building ELT workflows on Google Cloud with scheduling, monitoring, and error handling. Data architecture: medallion architecture (bronze/silver/gold), shared identifiers across systems (CRM record IDs, GA4 user IDs, UTM parameters), and consistent metric definitions. Version control and tooling: Git/GitHub, Visual Studio Code, and command-line tools (Bash, gcloud, bq). Experimentation: A/B testing, appropriate statistical tests (two-proportion z-tests, t-tests, chi-square tests), power analysis, sample-size calculation, confidence intervals, and avoiding pitfalls like early stopping and multiple comparisons. Dashboards and reporting: decision-oriented dashboards in Looker Studio and/or Tableau or Power BI, plus strong Google Sheets and Excel for analysis and stakeholder-ready outputs. Location and schedule Stanford, CA (onsite) . Compensation: USD 41–45 per hour. #J-18808-Ljbffr