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Key Responsibilities and Required Skills for Digital Performance Analyst

💰 $ - $

AnalyticsDigital MarketingE-commerceData Science

🎯 Role Definition

The Digital Performance Analyst is responsible for measuring, analyzing, and optimizing digital channels to improve acquisition, engagement, conversion, and lifetime value. This role combines advanced digital analytics (GA4, Adobe Analytics), tag management (GTM, Tealium), experimentation (A/B testing, CRO), attribution modeling, and cross-functional stakeholder communication to translate data into action and measurable revenue impact. The Digital Performance Analyst partners with marketing, product, UX, engineering, and finance to define KPIs, build dashboards, and deliver insights that drive marketing ROI and product adoption.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Digital Marketing Analyst / Web Analyst
  • Paid Media Analyst / PPC Specialist
  • Business Intelligence or Reporting Analyst

Advancement To:

  • Senior Digital Performance Analyst
  • Digital Analytics Manager / Head of Analytics
  • Conversion Rate Optimization (CRO) Lead
  • Growth Marketing Manager

Lateral Moves:

  • Marketing Data Scientist
  • Product Analyst / Growth Analyst
  • E‑commerce Analyst

Core Responsibilities

Primary Functions

  • Own end‑to‑end digital performance measurement for paid, organic, email, social, and affiliate channels: define KPIs, instrument tracking, validate data quality and deliver weekly/monthly performance reporting that highlights trends, anomalies, and optimization opportunities.
  • Design and maintain channel-level and holistic attribution models (rule-based, data-driven, and multi-touch) to quantify marketing ROI and inform budget allocation across acquisition and retention channels.
  • Implement, audit, and manage tagging plans using Google Tag Manager, Tealium, or server-side tagging; troubleshoot data-layer issues and ensure robust event capture across web, mobile apps, and SPA/PWA environments.
  • Configure and operate Google Analytics 4 (GA4) and Adobe Analytics: create custom events, parameters, user properties, segments, and audiences to power analysis, reporting, and activation.
  • Build scalable ETL pipelines and data models using SQL and BigQuery (or Snowflake/Redshift) to combine marketing, product, CRM, and revenue data into consolidated datasets for analysis and dashboards.
  • Lead A/B and multivariate testing programs using tools such as Optimizely, VWO, Google Optimize (or equivalent), from hypothesis formulation through experiment design, QA, analysis, and recommendations for rollout.
  • Create and maintain executive and operational dashboards in Looker, Tableau, Power BI, or Data Studio that track acquisition funnel performance, LTV, cohort retention, and marketing efficiency (CPA, CAC, ROAS, LTV:CAC).
  • Perform deep-dive analyses into funnel drop-off, session quality, page performance, and conversion drivers; provide prioritized testable recommendations to product and UX teams to increase conversion rates.
  • Translate complex data and statistical findings into concise, actionable recommendations for non-technical stakeholders — writing clear briefs, executive summaries, and decision-focused slide decks.
  • Monitor campaign tracking and paid media tagging (UTM governance) to ensure campaign performance is attributed correctly in analytics and ad platforms.
  • Partner with paid media and channel owners to forecast campaign performance, set measurable targets, and run budget scenarios that optimize for CPA, ROAS, or other business objectives.
  • Conduct regular data quality assurance (QA): perform regression checks, implement monitoring alerts for tracking breakages, and collaborate with engineering to remediate data integrity issues.
  • Lead cross-functional measurement initiatives for new product features, landing pages, and customer journeys; define instrumentation requirements and success metrics before launch.
  • Analyze cohort and retention metrics, build customer segmentation models, and identify high-value segments for activation and personalization efforts.
  • Use advanced analytics techniques (time-series analysis, uplift modeling, propensity scoring) to isolate incremental impact of marketing interventions and estimate incremental revenue.
  • Maintain up-to-date knowledge of privacy and compliance impacts on analytics (cookie deprecation, GTM server-side, consent management platforms, and CCPA/GDPR/PECR) and recommend implementation strategies.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to answer stakeholder questions and validate hypotheses.
  • Contribute to the organization's data strategy and roadmap by recommending analytics platform enhancements, governance policies, and measurement standards.
  • Collaborate with business units to translate data needs into engineering requirements and prioritize instrumentation and reporting work.
  • Participate in sprint planning and agile ceremonies within the data engineering and marketing teams to deliver analytics projects on schedule.
  • Provide training and documentation for marketing and product teams on how to interpret dashboards, use audiences, and leverage analytics tools for campaign optimization.
  • Maintain DMP/CDP integrations and audience synchronization workflows to enable activation of high-value segments in ad platforms and personalization engines.

Required Skills & Competencies

Hard Skills (Technical)

  • Google Analytics 4 (GA4) setup, event modeling, and reporting
  • Adobe Analytics (Workspace, Reports & Analytics) or equivalent
  • Google Tag Manager (GTM) and tag management strategy; server-side tagging familiarity
  • SQL for querying analytics and marketing datasets (BigQuery, Redshift, Snowflake)
  • Data visualization and dashboarding (Looker, Tableau, Power BI, Data Studio)
  • Experimentation platforms and CRO methodologies (A/B testing, multivariate testing)
  • Attribution modeling and media mix modeling fundamentals
  • Basic statistical analysis, hypothesis testing, and familiarity with R or Python for analytics
  • ETL/data pipeline understanding and working with marketing data lakes/data warehouses
  • Familiarity with paid media platforms (Google Ads, Meta Ads Manager, DV360) and UTM governance
  • Cohort analysis, retention metrics, and LTV modeling
  • CDP/DMP integration and audience activation workflows
  • Knowledge of web and mobile analytics implementation (SDKs, data layer, SPA tracking)
  • Understanding of privacy, consent frameworks, and their impact on measurement (GDPR, CCPA)

Soft Skills

  • Strong business acumen with the ability to tie analytics to revenue and commercial KPIs
  • Excellent stakeholder management across marketing, product, engineering, and finance
  • Clear written and verbal communication; ability to present technical findings to executives
  • Critical thinking and structured problem solving; hypothesis-driven approach
  • Prioritization and time management in a fast-paced, cross-functional environment
  • Curiosity and continuous learning mindset on emerging analytics and martech trends
  • Collaborative team player who can influence without direct authority
  • Attention to detail for QA and data validation tasks

Top transferable skills (SEO/LLM optimized): digital analytics, GA4, GTM, SQL, BigQuery, experimentation, CRO, attribution, data visualization, stakeholder management, privacy-compliant measurement.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Marketing, Statistics, Economics, Computer Science, Data Analytics, Mathematics, or related field.

Preferred Education:

  • Master’s degree in Data Science, Marketing Analytics, Business Analytics, Statistics, or MBA with analytics focus.

Relevant Fields of Study:

  • Marketing Analytics
  • Data Science / Applied Statistics
  • Computer Science / Software Engineering
  • Economics / Quantitative Social Sciences
  • Business Analytics

Experience Requirements

Typical Experience Range: 2–5 years of experience in digital analytics, web analytics, or a related analytics/marketing role working with GA4, GTM, experimentation, and SQL.

Preferred: 3–6+ years with demonstrable experience building analytics infrastructure, running experiments, owning attribution models, and delivering business-impacting insights in e-commerce, SaaS, or agency environments. Experience working with data warehouses (BigQuery, Snowflake), visualization tools (Looker/Tableau), and cross‑functional teams is highly desirable.

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