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Key Responsibilities and Required Skills for Digital Analyst

💰 $ - $

AnalyticsDigital MarketingData AnalysisWeb Analytics

🎯 Role Definition

The Digital Analyst is responsible for designing, implementing, validating, and interpreting digital measurement solutions that quantify user behavior, campaign performance, and business outcomes across web, mobile, and connected platforms. This role combines technical implementation (tagging, data quality, SQL), analytical interpretation (segmentation, attribution, funnel analysis), and stakeholder communication (dashboards, reports, recommendations) to drive conversion, engagement, and revenue growth.

Keywords: digital analyst, web analytics, GA4, Adobe Analytics, SQL, Google Tag Manager, marketing analytics, conversion optimization, data visualization, attribution modeling.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Web Analyst / Web Analyst
  • Digital Marketing Coordinator / Performance Marketing Associate
  • Business Intelligence Analyst (entry-level)

Advancement To:

  • Senior Digital Analyst / Lead Analyst
  • Analytics Manager / Marketing Analytics Manager
  • Product Analytics Lead or Conversion Rate Optimization (CRO) Manager
  • Director of Analytics / Head of Insights

Lateral Moves:

  • Business Intelligence (BI) Analyst
  • Conversion Rate Optimization (CRO) Specialist
  • Customer Analytics / CRM Analyst

Core Responsibilities

Primary Functions

  • Design, implement and maintain web and mobile analytics measurement plans that align with business objectives and KPIs, ensuring consistent taxonomy and event naming across channels.
  • Configure and manage Google Analytics 4 (GA4) properties, goals/events, custom dimensions/metrics, and auditing measurement to ensure accurate session and event tracking for multi-domain and single-page applications.
  • Implement and maintain tag management solutions (Google Tag Manager, Adobe Launch), authoring tags, triggers, and variables, and coordinating with engineering teams for deployment and release management.
  • Conduct ongoing data quality assurance (QA) and validation of analytics data pipelines, identifying and remediating tracking gaps, JavaScript errors, duplicate events, and cross-domain/session issues.
  • Build, maintain, and optimize interactive dashboards and recurring reports using Looker, Tableau, Power BI, or Data Studio to surface actionable insights for marketing, product, and executive stakeholders.
  • Use SQL to extract, transform, and analyze raw event and session-level data from BigQuery, Snowflake, or relational databases to enable advanced analysis and ad-hoc reporting.
  • Perform funnel, cohort, and retention analyses to identify conversion drop-offs, lifecycle segments, and opportunities to increase retention and lifetime value.
  • Lead campaign measurement and attribution analysis, implementing and evaluating attribution models (first/last touch, data-driven, media mix modeling) to quantify channel contribution to conversions and revenue.
  • Execute A/B and multivariate test analysis in Optimizely, VWO, or in-house experimentation platforms; design metrics, run statistical tests, and translate results into prioritized optimization recommendations.
  • Translate business and product requirements into technical analytics specifications and acceptance criteria; partner with product managers and engineers to ensure measurement completeness.
  • Monitor daily/weekly health and anomalies in traffic and conversion metrics; investigate sudden shifts, traffic anomalies, or data integrity issues and propose mitigations.
  • Perform deep-dive behavioral analysis using segmentation, pathing, session replay insights, and customer journey mapping to inform product and UX decisions.
  • Automate repetitive reporting workflows using SQL, scheduled queries, and reporting connectors to reduce manual effort and improve time-to-insight.
  • Create and maintain a measurement framework and governance documentation including event dictionaries, tag maps, naming conventions, and data retention policies.
  • Integrate analytics data with marketing platforms (DSP, ad platforms, CRM, CDP) to enable audience building, tracking, and attribution across paid and owned channels.
  • Provide consultative support to marketing and product teams on campaign tagging conventions (UTM), landing page setup, tracking pixels, and conversion tracking best practices.
  • Train business users and stakeholders on dashboard usage, metric definitions, and self-service analytics to increase data literacy across teams.
  • Collaborate with privacy, legal, and engineering teams to ensure analytics implementations comply with GDPR, CCPA, and consent management platform (CMP) requirements.
  • Maintain and optimize e-commerce measurement (Enhanced Ecommerce, revenue, SKU-level reporting), tracking revenue attribution and product performance across the funnel.
  • Evaluate and manage third-party analytics vendors and tools (heatmaps, session replay, tag governance) and recommend cost-effective solutions that meet measurement needs.
  • Produce regular insights narratives and executive-ready slide decks that translate analytic findings into prioritized, revenue-driving recommendations.
  • Partner with data engineering to improve schema design, event enrichment, and schema-driven analytics to support scalable, reliable downstream analysis.
  • Lead cross-functional analytics projects (e.g., GA4 migration, measurement platform consolidation), set timelines, and own delivery of analytics milestones.
  • Support customer lifetime value (LTV) and churn modeling initiatives by providing reliable behavior and transaction datasets, feature engineering, and retrospective analysis.
  • Maintain up-to-date knowledge of analytics platform updates, industry best practices, and emerging measurement approaches; recommend adoption where strategic.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

  • Google Analytics 4 (GA4): configuration, event modeling, measurement planning, and troubleshooting.
  • Adobe Analytics (Workspace) and/or Adobe Launch: implementation, reporting, and segmentation.
  • Google Tag Manager (GTM) and general tag management: container architecture, custom HTML tags, dataLayer management.
  • SQL: advanced querying for event/session-level analysis, joins, window functions, and performance optimization (BigQuery, Snowflake, Redshift).
  • Data visualization and dashboarding tools: Looker, Tableau, Power BI, Google Data Studio (Looker Studio).
  • A/B testing and experimentation platforms: Optimizely, VWO, Google Optimize (or in-house experimentation frameworks).
  • JavaScript fundamentals for debugging tracking snippets and implementing custom events.
  • Attribution modeling and media measurement techniques, including data-driven and multi-touch attribution.
  • Excel / Google Sheets: advanced formulas, pivot tables, and data manipulation for rapid analysis.
  • Basic scripting/data manipulation with Python or R for deeper analysis and automation.
  • Data warehouse and ETL familiarity: BigQuery, Snowflake, pipelines, and scheduled query operations.
  • Tag and event governance: event taxonomy, naming conventions, and measurement planning documentation.
  • API integrations: pulling/pushing analytics data to/from ad platforms, CRM, or CDP.
  • Familiarity with privacy regulations and consent management (GDPR, CCPA) and how they affect measurement.
  • Experience with e-commerce measurement (Enhanced Ecommerce or similar) and revenue attribution.

Soft Skills

  • Strong analytical thinking with the ability to translate complex data into clear, actionable recommendations.
  • Excellent stakeholder management: influence cross-functional teams and present insights to non-technical audiences.
  • Clear written and verbal communication; able to write concise executive summaries and technical specs.
  • Strong attention to detail and commitment to data accuracy and reproducibility.
  • Problem-solving mindset and comfort with ambiguity; can prioritize analyses that impact business outcomes.
  • Project management skills: roadmap planning, organizing deliverables, and meeting deadlines.
  • Collaboration and teamwork across product, engineering, marketing, and executive teams.
  • Curiosity and continuous learning orientation to stay current with analytics tools and practices.
  • Time management and multitasking across competing analysis requests.
  • Persuasive storytelling: transform data into narratives that drive decision-making.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master's degree in Data Analytics, Business Analytics, Data Science, Statistics, or an MBA with strong quantitative focus.

Relevant Fields of Study:

  • Marketing Analytics
  • Statistics / Applied Mathematics
  • Computer Science / Software Engineering
  • Data Science / Machine Learning
  • Economics / Quantitative Social Science
  • Business / Finance with analytics coursework

Experience Requirements

Typical Experience Range:

  • 2–5 years of hands-on digital analytics, web analytics, or marketing analytics experience.

Preferred:

  • 3+ years with demonstrated experience in GA4 and tag management, strong SQL skills, and a proven track record of delivering business impact through analytics and experimentation.
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