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Marketing Data Analyst

Hoover Institution, Stanford University Stanford, CA

Stanford, CA

Today

$41 - $45/hr Full time Marketing Bigquery Business Analytics

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Job Description

Hoover Institution at Stanford University is looking for an Audience & Marketing Insights Analyst to turn marketing data into decision-ready insights. In this role, you will build the pipelines, models, and measurement frameworks that help the organization understand reach, resonance, relationships, and what to change to improve outcomes. Success is defined by the quality of decisions your insights inform.

Stanford, CA (onsite) with an hourly rate of USD 41 to 45. The role requires 2 years of relevant experience and a bachelor’s degree in a relevant field (or an equivalent mix of education and quantitative analytical problem-solving).

What you’ll do

Build and maintain automated ELT pipelines that pull data from HubSpot, GA4, social, video, podcast, paid media, and event platforms into BigQuery using APIs and native integrations.
Organize marketing and audience data in BigQuery using a medallion architecture, and use Dataform transformations to convert raw and semi-structured sources (including 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 marketing, audience, CRM, web, and content data connect reliably.
Monitor pipeline health and data freshness so issues are caught early, and manage work with Git and GitHub for reproducibility and maintainability.
Ensure responsible handling of audience and contact data, credentials, and system access in line with Stanford privacy and information security policies and applicable regulations.
Support accurate campaign performance measurement by ensuring campaigns launch with the right tracking, tagging, and measurement plans, including consistent campaign naming and UTM conventions.
Partner with web developers to define GA4 event and data layer specifications, and QA tracking using tools such as Google Tag Manager preview mode and GA4 DebugView.
Troubleshoot tracking and data-quality issues across Hoover’s web team and external partners, including investigating discrepancies between GA4, HubSpot, and platform-reported figures.
Analyze who Hoover is reaching and how effectively priority audiences are engaged, using CRM, web, platform, event, and survey data.
Develop measurement frameworks and KPIs for reach, resonance, and relationships, distinguishing high-volume activity from engaged reading, viewing, and listening.
Build audience cohorts and segments based on acquisition source, content interests, behavior, and relationship to Hoover.
Run pre-, during-, and post-campaign analyses against objectives, and identify what audience, creative, message, format, timing, and platform elements drive performance.
Measure incremental impact of paid promotion using holdout groups, geographic tests, or platform lift studies where feasible; separate paid, organic, earned, and direct activity.
Assess marketing costs and outcomes, including cost per engaged visitor, new subscriber, and event registrant, to support evidence-based budget allocation.
Design and analyze A/B and multivariate tests across email, web, landing pages, paid media, messaging, and creative, with hypotheses, success metrics, minimum detectable effects, and required sample sizes.
Create automated cross-channel dashboards (for example, in Looker Studio built on BigQuery reporting tables) and produce regular reporting and executive-level analyses for nontechnical audiences.
Proactively identify patterns, opportunities, and risks in the data, then follow through to evaluate whether recommendations improved outcomes.
Contribute additional duties as needed.

What you bring

Bachelor’s degree in a relevant field and two 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 ability to combine data from multiple platforms and turn it into recommendations that shaped marketing, content, or audience decisions.
Ability to communicate analytical findings to nontechnical stakeholders.

Technical skills and tools

GA4 (event-based data model, custom events/parameters/custom dimensions, key events, Explorations) and correct interpretation of reporting impacts from attribution settings, consent mode, and data thresholding.
Data layer and Google Tag Manager, including tracking specifications and debugging with preview tools.
HubSpot experience with contacts, custom objects, properties, lifecycle stages, lists, marketing email, forms, and subscriptions, plus awareness of email open-rate limitations (including Apple Mail Privacy Protection).
UTM tracking, campaign taxonomy, Google Search Console, and native analytics for platforms such as YouTube Studio, Meta Business Suite, LinkedIn, and Google Ads.
BigQuery and Dataform for storing, querying, and transforming data (including nested/repeated fields), with cost-aware querying.
SQL for joins, window functions, CTEs, and working with JSON/text via regex and text functions.
Python and JavaScript for data processing, automation, and integration (including API calls, cleaning, statistical analysis, and GTM-related scripting).
REST API integration and ELT pipelines on Google Cloud, including authentication, pagination, rate limits, and monitoring/error handling.
Medallion architecture and mapping reporting logic to documented metric definitions using shared identifiers across systems (CRM record IDs, GA4 user IDs, UTM parameters).
Git/GitHub, Visual Studio Code, and command-line tools such as Bash, gcloud, and bq.
Experimentation and statistical testing experience, including A/B and multivariate testing with power analysis, confidence intervals, and correct use of two-proportion z-tests, t-tests, and chi-square tests.
Dashboarding in Looker Studio and/or Tableau or Power BI.
Google Sheets and Excel for quick-turn, stakeholder-ready work.

Preferred experience

Experience with cohort analysis, audience segmentation, funnel analysis, and audience journey analysis.
Experience with attribution modeling, incrementality testing, or marketing mix modeling (for example, 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 (such as Fivetran, Supermetrics, or Airbyte), Google Cloud services like Cloud Run/Cloud Scheduler/Secret Manager, and CI/CD tools such as GitHub Actions.
Experience using AI responsibly in line with institutional policy, including BigQuery ML or NLP workflows, and AI coding assistants to accelerate development.
Familiarity with web and UX analytics (such as Microsoft Clarity, Hotjar, Chartbeat, Parse.ly), SEO tools (Semrush, Ahrefs), survey tools (Qualtrics, SurveyMonkey), and social listening (Sprout Social, Brandwatch).
Experience in media, publishing, think tanks, nonprofits, higher education, or other mission-driven, content-focused organizations.
Interest in public policy, economics, history, or current affairs, and how ideas reach and influence audiences.

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