Data Analyst Sr

Why Valtech? We’re the experience innovation company - a trusted partner to the world’s most recognized brands. To our people we offer growth opportunities, a values -driven culture, international careers and the chance to shape the future of experience.

The opportunity

At Valtech, you’ll find an environment designed for continuous learning, meaningful impact, and professional growth. Whether you're pioneering new digital solutions, challenging conventional thinking or building the next generation of customer experiences, your work will help transform industries.

We are proud of:

The work we do and the innovation we drive

Our values of share, care a nd dare

A workplace culture that fosters creativity, diversity and autonomy

Our borderless, global framework, which enables seamless collaboration

The role

As a Data Analyst Sr , you are passionate about experience innovation and eager to push the boundaries of what’s possible. You bring 4+ YEARS of experience, a growth mindset and a drive to make a lasting impact.

You will thrive in this role if you are:

A curious problem solver who challenges the status quo

A collaborator who values teamwork and knowledge-sharing

Excited by the intersection of technology, creativity and data

Experienced in Agile methodologies and consulting (a plus)

Role responsibilities

Lead complex reporting, dashboarding, and performance analysis workstreams across multiple business areas, channels, or stakeholder groups.

Define measurement and reporting strategies that align business questions, KPI frameworks, semantic logic, and dashboard outputs.

Own the measurement framework for assigned accounts, including measurement and tracking plans, solution design references, event taxonomies, and naming conventions.

Set the technical direction for data collection and tag management, including Google Tag Manager, data layer specifications with engineering, and server side tagging where it improves data quality, performance, or privacy posture.

Define QA and validation standards for tracking work, maintain a martech and tag inventory per account, and recommend remediation when gaps or defects are found.

Define conversion, experimentation, and attribution measurement approaches in partnership with CRO, personalization, and media teams.

Protect measurement continuity during replatforming and migrations through pre migration baselines, parity checks, and post launch validation.

Own and guide dashboard ready data modeling, lightweight transformation logic, and semantic modeling practices needed to support scalable and consistent analytics outputs.

Establish and reinforce best practices for KPI design, reporting logic, metric definitions, and analytical documentation.

Identify trends, anomalies, performance drivers, and business opportunities through advanced analysis of structured data, and deliver executive ready insights that connect data to business action.

Serve as a senior advisor to client and internal stakeholders on measurement implications, reporting tradeoffs, and dashboard strategy, and act as the escalation point when reported numbers are questioned.

Model strong governance discipline across privacy, consent, data quality, and reporting standards, confirming the interpretation of regulatory obligations with the relevant client or Valtech owner.

Collaborate with Measurement Implementation Analysts, CRO Analysts, SEO / GEO Specialists, Analytics Engineers, Data Engineers, and Architects to align measurement and reporting solutions with business needs and technical realities.

Review and improve existing analytics deliverables, and build reusable assets such as measurement framework templates, tracking plan templates, and QA checklists.

Support proposal work, scoping and estimation, capability building, and internal thought leadership where analytical expertise is needed.

Must have qualifications

To be considered for this role, you must meet the following essential qualifications:

Deep working knowledge of digital analytics, measurement strategy, reporting strategy, dashboarding, and performance measurement.

Strong ability to define measurement and KPI frameworks, reporting logic, and analytical approaches in complex business environments.

Hands on expertise in tag management and data collection, including Google Tag Manager, data layer design, event taxonomies, and validation workflows.

Working knowledge of server side tagging concepts, first party data collection, identity and cookie behavior, and the tradeoffs involved in each.

Understanding of consent management and privacy expectations as they apply to data collection and reporting accuracy.

Advanced skill in translating data into clear, actionable, business relevant narratives for diverse stakeholder groups.

Strong understanding of semantic modeling and how metric definitions drive consistency and trust across reports and dashboards.

Strong analytical judgment, including the ability to identify root causes, business drivers, risks, and opportunities.

Ability to operate independently in ambiguous situations and bring structure to loosely defined analytics needs.

Strong written and verbal communication skills in English, including executive level presentation and stakeholder management capability.

Ability to influence without direct authority and elevate the quality of delivery across teams.

Strong attention to detail while balancing speed, practicality, and business usefulness.

Ability to collaborate effectively across distributed teams in the Americas and across multiple disciplines.

AI Fluency / AI Assisted Analytics Expectations

• Expected to be an active adopter of approved AI enabled analytics and productivity tools that improve the speed, clarity, and quality of reporting, insight generation, documentation, and stakeholder communication. Uses AI assisted workflows to help explore patterns, summarize findings, draft analysis narratives, validate assumptions, improve metric documentation, and accelerate repeatable reporting tasks while maintaining human accountability for analytical judgment, data accuracy, business interpretation, and final recommendations.
• Understands that AI generated analysis, summaries, or recommendations must be checked against source data, metric definitions, business context, and governance expectations before being shared or used for decision making. Demonstrates curiosity and practical enthusiasm for how AI can improve analytics quality, productivity, and decision support without replacing disciplined analysis or stakeholder reasoning.
• Applies the same discipline to measurement work: AI assisted drafts of tracking plans, tag configurations, or QA scripts are reviewed against the data layer, the measurement framework, and privacy expectations before implementation.
• At this level, AI fluency means senior judgment and standards influence within the role family. Expected to shape stronger AI assisted methods, reusable patterns, quality expectations, review practices, and stakeholder guidance while preserving human accountability, governance, and role family rigor.

Tools / Platforms

Analytics & Reporting

Google Analytics 4 (GA4)

Adobe Analytics

Other digital analytics platforms as needed

Tag Management & Data Collection

Google Tag Manager, web and server side

Adobe Experience Platform Tags, previously Launch

Tealium iQ and other enterprise tag managers as relevant

Customer data and event routing tools such as Segment, where present in the client stack

Data layer patterns and specifications, including ecommerce event models

Debugging and QA tooling such as GTM Preview, Tag Assistant, GA4 DebugView, browser developer tools, and network inspection

Consent & Privacy Tooling

Consent management platforms such as OneTrust, Didomi, Cookiebot, or the platform in use at the client

Consent Mode and reg