Divyaj S.
Performance Marketing Specialist
SUMMARY OF EXPERIENCE
• Divyaj has 4.5 years of paid media experience, with a strong focus on Meta and Google Ads across agency roles in Mumbai and New York. He's currently a Senior Paid Media Strategist at Django Digital, managing five brand accounts.
• At Django Digital, he improved Amazon ROAS from 7x to 9x within four months through bidding strategy optimisation, and delivered a 35% increase in Google Ads traffic alongside a 20% lift in conversions through custom audience segmentation.
• At With Clarity in New York, he managed paid media for a high-end jewellery brand, reducing cost per lead by 20%, improving lead quality by 30%, and achieving 3.5x ROAS within four months through refined targeting and creative testing.
👍 What we loved about them
• His phased campaign thinking: It's a genuine strength and possibly underappreciated at first glance. He consistently builds campaigns with a clear sequence in mind, tying each phase to a measurable outcome and explaining why the order matters. This translates well into client-facing work, where clients often want to understand the reasoning behind each decision, and he seems comfortable articulating that clearly.
• Prioritises tracking before everything: In both calls, without being prompted, Divyaj's first instinct when taking on any account was to check whether pixels, GTM, GA4, and enhanced conversions were properly set up and firing. This pattern came up naturally across multiple scenarios, including the Google account audit question, the LinkedIn scenario, and his case study walkthrough. We think this matters because a lot of performance marketers at this level treat tracking as something someone else handles, and then struggle to explain poor data quality later. The fact that he front-loads it consistently, and can articulate why it matters for platform learning algorithms, suggests it's genuinely baked into how he approaches his work rather than something he mentioned once for the sake of it.
• Cross-channel thinking is natural: Across both interviews, Divyaj independently brought up the relationship between Meta activity and Google branded search. He talked about monitoring whether people who see Meta ads start showing up as branded searches on Google, and factoring that into his Google budget decisions. A surprising number of candidates treat each platform in a silo and only connect them when prompted, so seeing him bring it up independently in two separate contexts was genuinely useful to observe.
ℹ️ Things to be aware of
• He has a 60-day notice period (potentially negotiable to 45 days).
• He has always operated in multi-client agency environments, and both his case study and his answers around prioritisation confirm he can hold several accounts in his head without dropping the basics or losing structure.
• Divyaj's hands-on experience is entirely B2C, specifically across jewelry, FMCG, clothing, and travel. While he was able to handle B2B-related questions reasonably well, his instinct to include Meta as a test channel felt like a B2C habit being carried into a context where it didn't quite fit.
• His Google Ads knowledge is competent but noticeably lighter than his Meta knowledge. When questions got specific around smart bidding, PMax, and search term strategy across both calls, his answers were less detailed and occasionally a bit more tentative.
💁♀️ Where he may need support
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Although he lacks extensive experience with LinkedIn and Bing ads, his proficiency in Google ads suggests a high adaptability to new platforms.
👩💻 Technical interview performance
Objective
This candidate was invited to a 60-minute follow-up interview to assess their technical capabilities in more detail. During this interview, we assessed their critical-thinking skills, technical expertise, and overall conversational skills.
Technical abilities
• PPC technical knowledge [8/10]: Divyaj has a solid and practical grasp of both Meta and Google Ads mechanics. On the Meta side, his reasoning around CBO versus ABO was clear and considered. He used CBO for prospecting because he wanted Meta's algorithm to distribute budget across untested audiences, while ABO gave him more control in retargeting where he already had confidence in the audience quality. His decision to avoid Advantage+ was well-reasoned too; he explained that the brief's requirement to specify which creative goes where made manual campaigns the right call, and he held that position confidently when pushed on it. His exclusion architecture was thorough, covering website visitors, engagers, email lists, and confirmed bookers across the relevant ad sets, in a way that felt like someone who has managed audience overlap issues in practice. On the Google side, he understood the distinction between branded and generic search campaigns, and his reasoning around exact and phrase match over broad was technically sound. His thinking on AI Max as an experimental lever, tested via a 50/50 traffic split over seven days, reflects genuine platform familiarity with how to introduce new bid types without disrupting a live campaign. We feel his Google depth is slightly lighter than his Meta depth, but for someone at this experience level, that's where we'd expect it to sit.
• Campaign strategy and full-funnel thinking [8.5/10]: His instinct to tie the pre-launch prospecting directly to a measurable action from day one was the right call for a 30-day window, and he explained that reasoning without needing to be prompted. His audience sequencing across the four Meta campaigns was logical and well-ordered, moving from cold interest-based audiences, to warm retargeting, to waitlist-to-booking conversion, through to lookalike prospecting in Phase 2. The messaging logic across phases was also distinct, with video-first content in pre-launch to build the experience, and UGC and testimonials in post-launch to drive urgency, which reflects a genuine awareness of where the audience is in their decision-making at each stage. Where we feel his thinking was particularly strong was in how he positioned Google relative to Meta. He treated search as a capture mechanism for intent generated upstream on Meta, which is the right read for a brand with limited organic search volume and a tight budget. His geo-prioritisation was also grounded in the data, using the €250 cost-per-booking ceiling to tier markets by efficiency, with a clear rationale behind each country grouping rather than just going by volume.
• Data interpretation and analytical thinking [8.5/10]: Divyaj's use of the client data in the media plan was practical and detailed. He anchored the geo-prioritisation framework around the €250 cost-per-booking ceiling, derived cleanly from the 50,000 euro budget divided by 200 bookings, then folded ROAS and cost efficiency together to flag markets like the UK and Germany as ones to monitor carefully despite their apparent size. In the interview, when pressed on a scenario where search volume was strong but CTR and CPC weren't following, he worked through it in a structured and methodical way, covering ad strength, impression share, page position, and landing page tracking in a logical sequence. He seemed genuinely comfortable moving through a diagnostic framework under a bit of pressure, and his answers felt considered rather than rehearsed.
• Adaptability and problem-solving under pressure [8.5/10]: Throughout the interview, Divyaj handled follow-up questions and hypothetical scenarios reasonably well. When asked what he'd do if search volume tripled, he walked through a considered reallocation of CPC savings from branded search into generic, choosing to optimize existing budget efficiency before assuming more spend was needed. When asked about audience overlap and cannibalization, he pulled directly from his exclusion strategy and extended it into a Google-specific new versus returning customer bidding approach, connecting the two channels together in a single answer. We feel he's someone who thinks on his feet reasonably well and stays composed when taken off-script. He acknowledged he hadn't run PMax in practice, that smart bidding hasn't delivered consistently strong results for him, and that his dashboard-building experience is still developing.
Areas of growth
• This probably sounds like nitpicking, but his lookalike strategy in Phase 2 felt a bit light. He mentioned using past purchasers as a seed audience but didn't get into how he'd validate the seed list size, or what he'd do if the pool wasn't large enough to generate a meaningful lookalike. His waitlist-to-booking conversion path also felt slightly reactive in the interview. He addressed it well when pushed, but the conversion value assignment and pixel logic around that handoff should ideally be part of the plan structure from the beginning.