Job Title:  T&T_Customer M&C - Senior Consultant | Adobe Target | Delhi

The team

Customer has to do much more than keep the wheels turning; it is the engine that drives functional excellence and the enabler of innovation and long-term growth. Learn more about: Customer

Key Responsibilities:

We are looking for an experienced Adobe Target & Data Collection expert to join our Martech and Digital Engineering team. The ideal candidate brings deep, hands-on expertise in Adobe Target experimentation and personalisation — across A/B testing, Multivariate Testing (MVT), Auto-Target, Automated Personalisation (AP), and Recommendations — combined with strong command of Adobe's data collection ecosystem: Adobe Experience Platform Tags (Launch), Web SDK (alloy.js), Adobe Analytics, and the Edge Network. You will be the technical owner of our experimentation platform and data collection infrastructure, enabling data-driven optimisation and personalisation at scale across web, mobile, and digital touchpoints.

1. Adobe Target — Experimentation & Testing:

• Architect, implement, and manage Adobe Target as the enterprise A/B testing and multivariate experimentation platform across web, mobile, and single-page application (SPA) environments
• Design, build, and QA A/B and A/Bn test activities — defining hypothesis, control/variant structures, traffic allocation, success metrics, and reporting audiences
• Implement Multivariate Testing (MVT) activities to evaluate multiple element combinations simultaneously and identify statistically significant winning experiences
• Configure Auto-Target activities that use machine learning to serve the best-performing experience to each visitor based on real-time behavioural and contextual signals
• Set up Automated Personalisation (AP) activities — defining offers, offer groups, and custom algorithms to serve 1:1 personalised experiences at scale
• Build Adobe Target Recommendations activities — configuring criteria (popularity, collaborative filtering, content similarity), entity attributes, design templates, and exclusion rules for product, content, and category recommendation carousels
• Define and manage Target audiences using visitor profile attributes, geography, technology, traffic sources, and custom parameters; build combined and sequential audience logic
• Implement experience and offer QA processes — URL-based QA links, profile script validation, mbox parameter debugging using browser developer tools and Adobe Experience Cloud Debugger

2. Personalisation Strategy & Implementation:

• Design and deliver server-side and client-side personalisation use cases using the Target Delivery API and on-device decisioning for ultra-low-latency, cookie-independent personalisation
• Leverage Target's Response Tokens to surface activity metadata, experience names, offer details, and algorithm information to analytics and reporting layers
• Implement 1:1 personalisation use cases: homepage hero personalisation, navigation personalisation, product listing page (PLP) sorting, cart and checkout optimisation, loyalty-tier messaging, and next-best-action content modules
• Integrate Target with Adobe Experience Platform RTCDP audiences — activating real-time AEP segments within Target activities for profile-enriched personalisation powered by unified customer data

3. Data Collection — AEP Tags (Launch) & Web SDK:

• Architect and manage Adobe Experience Platform Tags (Launch) as the enterprise tag management system — structuring properties, environments (dev/staging/prod), and publishing workflows for web and mobile surfaces
• Implement and configure AEP Web SDK (alloy.js) as the primary data collection and experience delivery mechanism — replacing legacy AT.js and AppMeasurement with a unified, Edge-first collection architecture
• Build Tags rule logic: page load rules, event-based rules (click, form submit, scroll depth, video engagement), direct call rules, and condition/exception logic using data elements and custom JavaScript
• Define and manage data elements — JavaScript variables, CSS selectors, local storage, cookie values, URL parameters, XDM objects — powering dynamic tag and SDK configurations
• Configure Datastreams in AEP to route Web SDK data to Adobe Analytics, Adobe Target, AEP (RTCDP), and Adobe Audience Manager — managing service-level overrides and event filtering
• Implement client-side and server-side (Edge) event forwarding — routing behavioural events to third-party destinations (Google Analytics 4, Meta Pixel, Mixpanel, Amplitude) via AEP Edge Network without additional client-side tags
• Enforce tag governance standards: property structure, naming conventions, publishing approval workflows, and environment-specific testing protocols

4. Adobe Target Recommendations:

• Design and configure Recommendations criteria — Most Viewed, Top Sellers, People Who Viewed/Bought, Recently Viewed, Trending, Content Similarity, and custom criteria using uploaded entity CSVs or API-fed catalogues
• Manage the Recommendations catalogue: entity attribute schema design (entity.id, entity.name, entity.categoryId, custom attributes), catalogue ingestion via feed files and Recommendations API, and entity update strategies
• Build Recommendations design templates using Velocity templating language — creating responsive, brand-compliant HTML/CSS carousel and grid layouts with dynamic entity attribute substitution
• Implement inclusion rules, exclusion rules, and dynamic filters (current category, current brand, profile attribute matching, parameter matching) to control recommendation quality and relevance
• Integrate Recommendations with Analytics for Target (A4T) to measure lift in revenue per visit, average order value, and conversion rate for recommendation experiences

5. Analytics for Target (A4T) & Reporting:

• Configure and validate Analytics for Target (A4T) integration — ensuring accurate activity impression, visit, and conversion data flows between Target and Adobe Analytics for unified reporting
• Build A4T-compatible success metrics: goal-based conversion metrics, revenue metrics (RPV, AOV), and engagement metrics mapped to Analytics events and eVars
• Implement Target activity reporting audiences in Analytics using segment-based breakdowns — enabling granular analysis of experience performance by visitor segment, channel, device, and geo
• Use Adobe Analytics Analysis Workspace to build Target activity performance dashboards — visualising lift, confidence, conversion rates, and revenue impact across test variants
• Define and govern statistical significance thresholds, minimum detectable effect (MDE) calculations, and sample size requirements in collaboration with data science and analytics teams

6. Experimentation Governance & Optimisation Programme:

• Establish and maintain an enterprise experimentation governance framework — hypothesis documentation, test prioritisation activity naming conventions, traffic allocation policies, and post-test analysis standards
• Manage the end-to-end test lifecycle: ideation, hypothesis, design review, technical implementation, QA, launch, monitoring, statistical analysis, and winner promotion or iteration
• Build and maintain a test backlog and roadmap in collaboration with UX, product, and marketing stakeholders — balancing revenue-impact experiments, personalisation programmes, and platform capability builds
• Implement and enforce mutual exclusivity and traffic segmentation strategies across simultaneously running Target activities to prevent test contamination
• Produce post-test readout documentation: executive summaries, statistical results, learnings, and recommendations for scaling winning experiences into personalisation rules

Skill / Technology

• Level Required
• Adobe Target (A/B, MVT, AP, Auto-Target, Recommendations)
• Expert
• AEP Tags (Launch) — Property Management & Rule Authoring
• Expert

NICE TO HAVE

• Adobe Certified Expert — Adobe Target or Adobe Analytics certification
• Experience with AEP Real-Time CDP audience activation into Adobe Target for profile-enriched personalisation
• Familiarity with Adobe Journey Optimizer (AJO) web personalisation and its relationship to Target activities
• Understanding of privacy regulations (GDPR, CCPA) and their impact on personalisation, consent management, and cookie strategies
• Experience with A/B testing statistical models — frequentist confidence intervals, Bayesian inference, and sequential testing frameworks

COMPETENCIES & SOFT SKILLS

• Analytical and hypothesis-driven mindset — comfortable translating business objectives into testable, measurable experiments
• Strong stakeholder communication skills — able to articulate technical implementation decisions and test results to marketing, product, and commercial audiences
• Detail-oriented with a quality-first approach — meticulous about implementation accuracy, data integrity, and QA before launch
• Collaborative team player — experienced working across UX, analytics, engineering, and marketing operations in agile delivery environments
• Self-starter who proactively tracks Adobe product releases, beta features, and industry developments in experimentation and personalisation

Location and way of working:

• Base location: Delhi  
• Education: Professional Qualification - B.E./B.Tech/MCA/MBA/MS
• This profile involves occasional travelling to client locations.
• Hybrid is our default way of working. Each domain has customized the hybrid approach to their unique needs.