Key Responsibilities and Required Skills for Web Analyst

🎯 Role Definition

The Web Analyst is a data-driven professional who designs, implements, and maintains web analytics measurement frameworks, translates raw event and session data into clear insights, and partners with product, marketing, UX and engineering teams to optimize acquisition, engagement and conversion. The role requires hands-on experience with analytics platforms (GA4, Adobe Analytics), tag management (GTM), SQL and dashboarding tools (Looker, Tableau, Power BI), a bias toward experimentation and testing, and the ability to communicate complex results in business terms.

📈 Career Progression

Typical Career Path

Entry Point From:

Junior Web Analyst / Analytics Coordinator
Digital Marketing Analyst or SEO Specialist with strong analytics focus
Data Analyst with emphasis on digital product metrics

Advancement To:

Senior Web Analyst / Lead Web Analyst
Analytics Manager / Digital Analytics Manager
Conversion Rate Optimization (CRO) Manager
Product Analytics Manager

Lateral Moves:

Data Engineer (with focus on analytics pipelines)
Growth/Performance Marketing Manager
UX Researcher / Product Manager (analytics-driven)

Core Responsibilities

Primary Functions

Design and maintain a comprehensive web analytics measurement plan and event taxonomy aligned with business KPIs (acquisition, activation, retention, revenue, referral), ensuring consistent naming conventions and documentation for GA4, Adobe Analytics and other tracking systems.
Implement and QA analytics tags and event tracking using Google Tag Manager (GTM), dataLayer design and custom JavaScript, coordinating with front-end engineers to ensure accurate capture of pageviews, events, e-commerce transactions and user attributes.
Lead Google Analytics 4 (GA4) setup, configuration and migrations from Universal Analytics, including event mapping, custom dimensions and metrics, data streams, and enhanced measurement to ensure continuity of reporting.
Configure and validate Adobe Analytics reporting suites, props/eVars and processing rules when applicable, and maintain tag governance across multiple analytics technologies.
Write and maintain SQL queries to extract, transform and aggregate clickstream and backend data from analytics warehouses (BigQuery, Snowflake, Redshift) to produce ad-hoc analyses, funnel metrics and cohort studies.
Build and maintain interactive dashboards and executive-level scorecards in Looker, Tableau, Power BI or similar, enabling stakeholders to self-serve on acquisition, funnel, cohort and revenue reporting.
Perform funnel and conversion rate optimization (CRO) analyses to identify drop-off points, friction in user journeys and prioritized test opportunities; partner with design and product to plan experiments.
Design, analyze and report on A/B tests and multivariate experiments using experimentation platforms (Optimizely, VWO, Google Optimize/GSN) including pre/post power calculations, significance testing and confidence intervals.
Conduct attribution analysis across channels (paid search, display, social, email, organic) and recommend changes to channel investment using multi-touch attribution models and incrementality methods.
Segment user behavior to produce cohort analyses, RFM (recency-frequency-monetary) reports and lifetime value (LTV) models to inform retention and acquisition strategies.
Monitor data quality, detect anomalies and resolve tracking or instrumentation issues; own the analytics backlog for bug fixes, versioning and cross-team dependencies.
Translate analytics outputs into clear, actionable business recommendations and prioritization for product and marketing roadmaps; write data-backed briefs for experiments and feature changes.
Implement and enforce analytics data governance policies including cookie/consent management, PII avoidance, sampling mitigation and retention policy adherence with Privacy and Legal teams.
Maintain and document tagging libraries, implementation guides and runbooks; train business users and cross-functional teams on metrics definitions and how to interpret dashboards and reports.
Perform site performance and UX analytics including page load and engagement metrics; advise on technical and content changes to improve conversions and accessibility.
Automate routine reporting and alerting using scheduled queries, Looker/Power BI subscriptions, and Slack/email notifications to surface KPI regressions and opportunities.
Work with data engineering to design event schemas and ingestion pipelines, ensuring analytics events are structured for both real-time and historical analysis.
Derive and maintain business-critical derived metrics (ARPU, CAC, churn rate, conversion funnels) and ensure consistent implementation across dashboards and analyses.
Support marketing campaign measurement: UTM tracking validation, landing page performance analysis, conversion attribution and ROI reporting across paid and owned channels.
Conduct customer journey mapping based on behavioral data to identify micro-conversions, friction points, and high-value journeys for personalization and growth initiatives.
Prepare executive summaries and narrative-driven insights for leadership, turning quantitative results into strategic recommendations and prioritized next steps.
Participate in cross-functional planning to align analytics capabilities with roadmap initiatives, sprint planning and release timelines.
Troubleshoot complex cross-platform tracking issues that affect aggregated reporting, such as duplicate events, bot traffic, cross-domain tracking and session stitching.
Stay current with industry trends, analytics platform updates (e.g., GA4 releases), privacy regulation changes (GDPR, CCPA) and recommend tooling or process changes to maintain best practices.

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.
Mentor junior analysts and help develop analytics training materials.
Participate in vendor evaluations for analytics, experimentation and customer-data platforms.

Required Skills & Competencies

Hard Skills (Technical)

Google Analytics 4 (GA4) — measurement strategy, event configuration, custom dimensions/metrics, reporting and migration experience.
Adobe Analytics (Desirable) — report suite configuration, eVar/prop implementation and workspace reporting.
Google Tag Manager (GTM) and tag management — container design, custom HTML tags, triggers, variables, and dataLayer planning.
SQL — intermediate to advanced query writing for aggregation, joins, window functions and cohort/funnel analysis (BigQuery, Snowflake, Redshift).
Data visualization — Looker, Tableau, Power BI or equivalent for dashboard design and data storytelling.
A/B testing and experimentation platforms — Optimizely, VWO, Google Optimize or equivalent; familiarity with test design and statistical analysis.
HTML/CSS and JavaScript fundamentals — ability to collaborate with front-end engineers and implement/QA tags.
Familiarity with data warehouses and ETL concepts — BigQuery, Snowflake, streaming vs batch, schema design for event-level data.
Excel/Google Sheets advanced skills — pivot tables, formulas, lookup functions and automation.
Scripting for analytics — basic Python or R for data processing, automation and advanced analysis.
Attribution modeling and marketing mix modeling basics — understanding of multi-touch attribution, last-click limitations and incrementality methods.
Consent and privacy tools — basic knowledge of CMPs, GDPR/CCPA implications for analytics collection.
API integration experience — pulling data from advertising platforms (Google Ads, Meta) and sending data to reporting platforms.
Knowledge of SEO metrics and web performance tools (Core Web Vitals, PageSpeed Insights) to correlate analytics with search performance.

Soft Skills

Strong business acumen with the ability to translate analytics into commercial impact and prioritized recommendations.
Excellent written and verbal communication: craft executive summaries, present findings to stakeholders and build narratives around data.
Problem-solving mindset: proactive in identifying root causes, designing experiments and validating hypotheses.
Collaboration and stakeholder management: work cross-functionally with product, engineering, marketing and legal teams.
Attention to detail and quality orientation for instrumentation, QA and data governance.
Time management and prioritization to balance recurring reporting, ad-hoc analyses and project work.
Curiosity and continuous learning: keep up with analytics trends, tools and privacy regulations.
Coaching and mentorship skills to support junior team members and promote analytics literacy across the organization.
Adaptability in fast-paced environments and comfort with ambiguity when building measurement for new products or features.
Ethical judgment regarding user data and privacy, and a bias toward compliant and transparent data practices.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Marketing, Information Systems, or a related quantitative discipline.

Preferred Education:

Master's degree in Data Science, Analytics, Business Analytics, or an MBA with a strong analytics concentration is a plus.

Relevant Fields of Study:

Statistics / Applied Mathematics
Data Science / Computer Science
Marketing / Digital Marketing
Economics / Business Analytics
Information Systems / Engineering

Experience Requirements

Typical Experience Range: 2–5 years of hands-on web or digital analytics experience (analytics implementation, reporting, and A/B testing).

Preferred: 3–7+ years with demonstrated experience in GA4 and tag management, SQL-based analysis, dashboarding, and running/analysing experiments in a SaaS, e-commerce, media or high-traffic consumer product environment.