Marketing Data Analyst

What you'll do: Build and maintainautomated ELT pipelinesthat move data from Hub Spot, GA4, social, video, podcast, paid media, andevent platformsinto Big Queryusing APIs and native integrations, reducing manual exports. Organize marketing and audience data in Big Query using amedallion architecture, and use Dataformtransformations to convert raw and semi-structured inputs (including GA4 event export and JSON API responses) into cleaned, reporting-ready tables. Create and maintain adata dictionarydocumenting data models, shared identifiers, transformation rules, andmetric definitionsso marketing, audience, CRM, web, and content data connect reliably. Monitor pipeline health anddata freshness, ensuring failures are caught before they impact reporting, and manage code with Git and Git Hubfor reproducibility and collaboration. Handleaudience and contact data, credentials, and access responsibly and in line with Stanford privacy and information security policiesand applicable regulations. Ensure campaigns launch with propertracking, tagging, and measurement plans, including consistent campaign naming andUTM conventions. DefineGA4 event and data layer specificationsin partnership with web developers, then QA tracking using tools such as Google Tag Manager preview modeandGA4 Debug View. Troubleshoot tracking anddata-quality issueswith Hoover's web team and external partners, and investigate discrepancies between GA4, Hub Spot, 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 forreach, resonance, and relationships, separating high-volume activity from engaged reading, viewing, and listening. Analyzecross-channel journeysfrom 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 throughpre-, during-, and post-campaignanalyses aligned to defined objectives, and identify what content, topics, fellows, formats, and campaigns drive acquisition and repeat engagement. Measure incremental impact of paid promotion usingholdout 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 designA/B and multivariate testsacross email, web, landing pages, paid media, messaging, and creative. Translate analysis intoclear, actionable recommendationsthat influence decisions about content prioritization, distribution, audience acquisition, and investment. Build automated cross-channel dashboards (for example, Looker Studioon Big Query 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 degreein a relevant field and 2 yearsof relevant experience, or a combination of education and relevant experience solving analytical problems using quantitative approaches. Experience inmarketing analytics, audience analytics, ordigital analytics, working directly with marketing, communications, or content teams. Demonstrated experience combining data from multiple platforms and using it to shapemarketing, content, oraudiencedecisions. Ability tocommunicate analytical findingstonontechnical stakeholders. Technical and analytics skillsGA4 (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. Hub Spot:pulling and analyzing contacts, custom objects, properties, lifecycle stages, lists, marketing email, forms, and subscriptions, including awareness of limitations such as Apple Mail Privacy Protectioneffects on open rates. Campaign tracking: UTM tracking, campaign taxonomy, Google Search Console, and native platform analytics (You Tube Studio, Meta Business Suite, Linked In, Google Ads) with understanding of metric definitions like views, reach, and engagement. Big Query & 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 Java Script: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/Git Hub, 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 Excelfor analysis and stakeholder-ready outputs. Location and schedule Stanford, CA (onsite).

Compensation: USD41–45per hour.