Marketing Data Analyst
# Marketing Data Analyst **Hoover Institution, Stanford University** · Stanford, CA 94305 · `On-site` 🕒 **Статус:** *Опубликовано: 4 дня назад* · *Источник: Indeed* --- ### About the Role JOB PURPOSE: The Hoover Institution at Stanford University is seeking an Audience & Marketing Insights Analyst to join its Marketing and Communications team. The role is part marketing analyst, part analytics engineer: the analyst builds the pipelines and data models that connect Hoover’s platforms, then uses that data to answer four questions: Who are we reaching? Which audiences is Hoover reaching across its website, email, video, podcasts, social platforms, events, and paid media, and how effectively is it reaching the audiences most important to its mission? What resonates? Which content, campaigns, fellows, topics, and formats meaningfully connect with those audiences, and why? What deepens relationships? Which actions and experiences move people from a single encounter with Hoover to repeat engagement, subscriptions, event attendance, and a lasting, direct relationship with the Institution? What should we do differently? How should those insights change Hoover’s decisions about content, distribution, audience acquisition, and marketing investment? The analyst will move beyond reporting what happened to explain why it happened, what it means, and what Hoover should do next. Success in this role is measured not by the volume of analysis produced, but by the quality of the decisions it informs. The ideal candidate pairs genuine marketing judgment (about how audiences discover ideas, how content and campaigns are planned and distributed, and how marketing teams make decisions) with the hands-on technical skills to build and use the data foundation behind that judgment: connecting Hoover’s platforms through APIs and ELT pipelines, modeling data in BigQuery and Dataform, interpreting GA4 and HubSpot data correctly, and applying the right statistical tests. This position reports to the Senior Manager, Digital Growth Strategy and works cross-functionally across Hoover’s Marketing and Communications organization. CORE DUTIES Data Infrastructure & Pipelines 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. Measurement & Tracking 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. Audience Intelligence: Reach, Resonance & Relationships 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. Analyze cross-channel audience journeys from social media, search, paid media, video, podcasts, and earned exposure into Hoover-owned channels such as Hoover.org, email, and events. Develop audience cohorts and segments based on acquisition source, content interests, behavior, and relationship with Hoover. Identify which topics, fellows, formats, and campaigns attract new audiences and generate repeat engagement, and which actions (such as newsletter sign-ups, return visits, and event attendance) signal and drive deeper relationships. Campaign Performance & Marketing Investment Conduct pre-, during-, and post-campaign analyses to assess performance against defined objectives and identify opportunities for improvement. Determine which campaign elements (audience, creative, message, fellow, topic, platform, format, and timing) contribute most to performance, building institutional knowledge about what works, for whom, and under what circumstances. Measure the incremental impact of paid promotion, using holdout groups, geographic tests, or platform lift studies where feasible, and distinguish paid, organic, earned, and direct audience activity. Analyze marketing costs and outcomes, including cost per engaged visitor, new subscriber, and event registrant, to inform evidence-based budget allocation. Partner with marketing leadership to establish campaign objectives and measurement plans before major initiatives launch. Experimentation & Testing Design and analyze A/B and multivariate tests across email, web, landing pages, paid media, messaging, and creative. Define hypotheses, primary success metrics, minimum detectable effects, and required sample sizes (through power analysis) before launch, then apply the appropriate statistical tests to evaluate results for both statistical and practical significance. Maintain a record of experiments and findings so that insights inform future campaigns. Insights, Dashboards & Reporting Translate complex data into clear, actionable recommendations that shape Hoover’s decisions about content (which topics, fellows, and formats to prioritize), distribution, audience acquisition, and investment. Develop and maintain automated, cross-channel dashboards (e.g., in Looker Studio, built on reporting-ready tables in BigQuery), including self-service views that let content producers and program teams see how their work is performing. Develop regular reporting and executive-level analyses that highlight not only metrics but also key findings, implications, and recommended actions for nontechnical audiences. Proactively identify patterns, opportunities, and risks in the data rather than relying solely on requests for analysis, and follow up on recommendations to evaluate whether the changes improved outcomes. Other duties may also be assigned. MINIMUM REQUIREMENTS Education & Experience 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 experience combining data from multiple platforms and translating it into recommendations that shaped marketing, content, or audience decisions. Experience communicating analytical findings to nontechnical stakeholders. Knowledge, Skills & Abilities Solid grounding in marketing fundamentals, including audience development, content marketing, email and lifecycle marketing, social and video distribution, search, and paid media, and an understanding of how analytics should inform decisions in each. Strong analytical skills, demonstrated curiosity about the meaning behind data, and the ability to move from data to insight to recommendation, not simply report metrics. Ability to distinguish between correlation and causation and clearly communicate the limitations of available data. Exceptional ability to tell a clear, compelling story with data (visually, verbally, and in writing) for nontechnical audiences, including senior leadership and faculty. Strong attention to detail and commitment to data integrity. Ability to work independently, collaborate across teams and disciplines, and balance quick-turn requests with longer-term analytical work. REQUIRED TECHNICAL SKILLS The analyst must be able to independently connect, model, and analyze Hoover’s marketing and audience data, and to build and maintain the data foundation that supports this work. Marketing & Web Analytics Platforms Google Analytics 4: Advanced proficiency with GA4’s event-based data model (custom events, parameters, custom dimensions, key events, and Explorations) and the ability to interpret GA4 numbers correctly, including how attribution settings, consent mode, and data thresholding affect what is reported. Data layer and tag management: Working knowledge of the data layer and Google Tag Manager, with the ability to write tracking specifications and debug implementations. HubSpot and CRM data: Hands-on experience pulling and analyzing HubSpot data (contacts, custom objects, properties, lifecycle stages, lists, marketing email, forms, and subscriptions) through reports, exports, or the API, and an understanding of the limits of email engagement metrics (such as the effect of Apple Mail Privacy Protection on open rates). Campaign tracking and platform analytics: Strong working knowledge of UTM tracking, campaign taxonomy, Google Search Console, and native platform analytics (e.g., YouTube Studio, Meta Business Suite, LinkedIn, and Google Ads), including how each platform defines and counts metrics such as views, reach, and engagement. Analytics Engineering & Development Cloud data warehousing and transformation: Using Google BigQuery and Dataform to store, query, and transform data, including nested and repeated fields such as the GA4 BigQuery export, Dataform workflows with dependencies and data-quality assertions, and cost-aware queries. SQL: Developing queries and data transformations, including joins across sources, window functions, common table expressions, and JSON, regular-expression, and text functions. Python and JavaScript: Writing scripts for data processing, automation, and system integration, such as Python for API calls, data cleaning, and statistical analysis, and JavaScript for Google Tag Manager, Google Apps Script, or Dataform. API integration and ELT pipelines: Retrieving data through REST APIs (e.g., HubSpot, the GA4 Data API, YouTube, and Meta), including authentication, pagination, and rate limits, and building ELT workflows on Google Cloud with scheduling, monitoring, and error handling. Data architecture and modeling: Applying medallion architecture (bronze, silver, and gold layers) and mapping business requirements into documented reporting logic, with shared identifiers across systems (such as CRM record IDs, GA4 user IDs, and UTM parameters) and consistent metric definitions. Version control and development tools: Using Git and GitHub (branches, commits, and pull requests), a coding IDE such as Visual Studio Code, and command-line tools (Bash, gcloud, and bq). Analysis, Testing & Visualization Experimentation and statistical testing: Experience designing and evaluating A/B tests, including correctly applying statistical tests (two-proportion z-tests, t-tests, and chi-square tests), power analysis, sample-size calculation, and confidence intervals, and avoiding pitfalls such as stopping tests early and multiple comparisons. Data visualization: Proficiency building clear, decision-oriented dashboards in Looker Studio and/or tools such as Tableau or Power BI, grounded in data-visualization best practices. Spreadsheets: Advanced proficiency in Google Sheets and Excel for quick-turn analysis and stakeholder-ready outputs. PREFERRED/DESIRED KNOWLEDGE, SKILLS & ABILITIES 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. Interest in public policy, economics, history, or current affairs, and in how ideas reach and influence audiences. The expected pay range for this position is $41-45 per hour. Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location, and external market pay for comparable jobs. Job Types: Full-time, Temporary Pay: $41.00 - $45.00 per hour Ability to Commute: - Stanford, CA 94305 (Preferred) Ability to Relocate: - Stanford, CA 94305: Relocate before starting work (Preferred) Work Location: In person
Наблюдалась 2026-10-07, впервые 2026-10-02, источник — Indeed.