Digital Specialist

ESSENTIAL DUTIES AND RESPONSIBILITIES include, but are not limited to, the following:

Decision Support and Insights

Build and maintain core e-commerce KPI dashboards: conversion rate, revenue per visit, AOV,

funnel drop-off rates, site search performance, and campaign rollups.

Produce a weekly performance narrative - a concise written summary with recommended actions, not just charts - distributed to the VP of Digital Product and key stakeholders.

Analyze site changes, A/B test results, and UX experiments; advise on metric selection and

hypothesis framing before tests launch, then own the readout.

Partner with Marketing to define campaign success metrics upfront and deliver post campaign analysis tied to business outcomes, not vanity metrics.

Provide seasonal and competitive context that frames performance data in a broader story.

Anomaly Detection and Data Quality

Design and maintain monitoring alerts for traffic, conversion, and revenue anomalies - define the thresholds, own the system.

Develop a repeatable root-cause playbook to distinguish between tracking failures, behavioral

shifts, marketing mix changes, and inventory, pricing, or promo effects. Investigate unexpected patterns proactively; don't wait for a stakeholder to notice something is wrong.

Flag and escalate tagging or data integrity issues to Engineering with enough context to act on immediately.

Measurement & Instrumentation

Conduct regular audits of the GA4 implementation: event taxonomy, conversion configurations, data layer values - identify what's misfiring, missing, or inconsistent.

Translate audit findings into developer-ready specs: document exactly what's broken, what correct behavior looks like, and what Engineering needs to fix at the application level.

Run a tagging QA checklist after every significant Engineering release to catch regressions before they corrupt reporting data.

Validate conversion tracking accuracy after site updates; communicate urgency and prioritize fixes by reporting impact.

Where GTM access is granted, execute straightforward implementation changes directly - tag updates, trigger adjustments, GA4 event configurations. Escalate data layer changes to Engineering with a written spec.

Dashboard & Reporting

Own Tableau as the primary reporting layer for the business; build dashboards that executives and stakeholders can use independently, not just analysts.

Use SQL for ad-hoc analysis, data validation, and enriching analytics data - joins, aggregations, and filtering, not pipeline engineering.

Establish and document KPI definitions to maintain a single source of truth across Product,

Marketing, and Leadership.

Deliver insights in plain language with clear recommendations; raw data tables without narrative are not a deliverable.

SKILLS AND QUALIFICATIONS

Qualifications

Graduate with a bachelor's degree in Data Analytics, Statistics, Business Analytics, Information Systems, Marketing Analytics, or any related field.

Expert-level proficiency in Google Analytics 4: event-based data model, custom explorations,

segments, conversion configuration, and implementation quality assessment.

Proficient in SQL for data validation, ad-hoc analysis, and cross-source data joining - aggregations, filters, joins, CTEs.

Hands-on Tableau experience building executive-ready dashboards.

Demonstrated ability to conduct e-commerce funnel analysis, multi-channel performance

analysis, and structured root-cause investigation.

Self-directed with a clear point of view about how data should inform decisions; comfortable

pushing back when the data doesn't support a conclusion.

Experience and Certifications

Minimum 5 years of experience in digital analytics, web analytics, or marketing analytics; e-

commerce or direct-to-consumer experience is strongly preferred.

Ecommerce or Marketing certifications are a plus.

Technical or specific skills (e.g. technical, computer)

Strong written and verbal communication skills; able to translate analysis into executive-ready

recommendations without leaning on jargon or raw output.

Able to write clear, developer-ready measurement requirements and QA analytics instrumentation against a data layer spec - you don't need to write JavaScript, but you need to know what correct tracking looks like and document the gap precisely.