Shanya J.
Performance Marketer
SUMMARY OF EXPERIENCE
• Shanya has ~2.5 years of paid media experience across Google, Meta, LinkedIn, and DV360, progressing from executive to team lead at Amura Marketing Technologies, where she currently oversees a team of eight managing multi-industry portfolios.
• At Amura, she reduced CPL by 35% for a B2B brand, delivered 1,200+ student admissions for an education client, built CAPI and offline conversion tracking across multiple brands, and contributed to ₹150Cr in revenue across two quarters.
• At GO MO Group, she managed end-to-end paid campaigns across Google, Meta, and DV360, improving conversion rates by 25% and hitting 120% of the visitor target for a global consumer electronics brand through structured audience and creative optimisation.
👍 What we loved about them
• Strong strategic instincts: We think Shanya is at her strongest when she's operating at a strategic and structural level across paid media. Her ability to tier markets by unit economics, build phased campaign architecture, and manage client relationships as the primary point of contact reflects someone who has had to own outcomes, not just execute tasks. The team lead experience at Amura, where she was managing eight marketers across 50-plus accounts, has clearly sharpened her ability to think about campaigns holistically and make resource allocation decisions quickly. Her Meta instincts in particular are sharp and grounded in real account experience.
• She's logical and opinionated: When asked how she would handle a client who wanted to copy a competitor's strategy and simply outbid them, she didn't just go along with it. She acknowledged what was reasonable about the instinct, then explained clearly why replicating a competitor's keyword list wholesale would inflate CPCs without delivering the same structural advantage, since the competitor already has their quality score, SEO, and account history working in their favour. She suggested a competitor campaign as a measured alternative to test specific terms rather than a wholesale copy. This is one of many examples where we found her to be calm, confident, and logical.
• Structured in her approach: Her decision to split the campaign into three distinct phases, with separate objectives, creative direction, and audience logic at each stage, gave her plan more operational clarity than a basic pre and post-launch split would have. Separating the initial urgency push around booking open from the sustained conversion phase was a deliberate and sensible call, because the creative messaging and audience mix genuinely shifts between those two windows. She articulated this clearly without being prompted, and it made the plan feel executable rather than just directionally correct.
ℹ️ Things to be aware of
• Shanya's available to join immediately.
• She left her previous role last week as a result of a company merger pushing her team toward CRM work outside her domain; it wasn't a performance-related exit.
• Comfortable across the full performance stack, including Google Ads, Meta Ads, LinkedIn Ads, DV360, GA4, GTM, Supermetrics, and Looker Studio, with hands-on experience in reporting automation.
💁♀️ 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]: We think Shanya has a solid and well-rounded technical foundation across both platforms. On Google, her campaign structure was sensible and deliberate, with branded search on exact and phrase to protect the SERP, non-branded on phrase and broad for category demand, and a conscious decision to leave PMax out given the budget and 30-day window. When pushed on PMax, she gave an accurate answer about audience signal prerequisites and explained how she'd use channel performance reporting to prevent cannibalisation. Her search term mining framework was also structured, covering converting keywords, keywords spending without converting, and keywords with no spend at all as three distinct buckets with different actions attached. On Meta, she came in with noticeably stronger instincts. Her audience architecture was well-layered, moving from broad interest and lookalike prospecting through to video viewer retargeting, with clear exclusions at each stage to prevent overlap. She knew exactly when and why to refresh creative, citing frequency thresholds and placement-level performance as diagnostic triggers rather than defaulting to a calendar-based cadence.
• Campaign strategy and full-funnel thinking [8.5/10]: This is where Shanya is at her clearest and most confident. The three-phase structure she built was well-reasoned and operationally thoughtful, moving from waitlist prospecting through urgency-led conversion to a sustained push with creative scaling decisions built in. What impressed us most was the expat audience layer – she pulled the specific stat that 21% of non-US guests were expats, built a dedicated ad set using Meta's "living abroad" and "away from hometown" behavioural signals, and applied it to markets like UAE, Switzerland, and France where that audience is concentrated. Her geo-tiering logic was also data-driven, prioritising US, Australia, Switzerland, and UAE based on ROAS and cost-per-yacht figures from the client data, and explicitly deprioritising markets like the UK and Brazil where the unit economics were poor. On Google, she positioned it correctly as a complementary capture channel for branded spillover traffic from Meta, which we felt was the appropriate strategic framing for a product like this. The one area that felt slightly underdeveloped was the bottom-of-funnel conversion strategy, where her answers on how she'd push people from lead to confirmed booking stayed fairly general.
• Data interpretation and analytical thinking [8/10]: Shanya read the client data carefully and used it with purpose. The geo-tiering decision was grounded directly in the ROAS and cost-per-yacht figures from the FY17-19 dataset, and she made a deliberate call to demote the UK from Tier 1 after identifying its cost-per-yacht of 562 euros versus 141 euros for the US. She also correctly arrived at a blended CPA target of 250 euros by dividing the total budget by the booking target. In the interview, her approach to diagnosing campaign underperformance was layered, starting with search term analysis, then ad copy relevance, then landing page quality. Her reporting framework, pulling campaign, ad set, and ad-level data into a unified Supermetrics dashboard and cross-referencing with GA4, is a practical approach that shows she's built her own process for reconciling platform data.
• Adaptability and problem-solving under pressure [7.5/10]: Across both interviews with a healthy amount of follow-up probing, Shanya held up well. The question about a client wanting to cut Meta entirely because Google's cost per qualified lead was three times lower was a well-constructed trap, and she navigated it confidently. She explained the push versus pull platform dynamic clearly, brought in attribution modelling as evidence for Meta's contribution, and then pivoted to auditing Meta's creative performance rather than simply defending the channel – we found this to be one of her stronger moments in the interview. She also handled the competitor strategy scenario well, acknowledging what was reasonable about the client's instinct before explaining why copying a competitor's keyword list wholesale would likely inflate CPCs without delivering the same return. She covered CPC, CTR, and bid strategy adjustments but didn't produce a clear diagnostic sequence. We think this is a confidence and practice issue rather than a knowledge gap, and one that will resolve quickly with more live client exposure.
Areas of growth
• Shanya understands the importance of cross-platform attribution and she knows the right tools to use, including GA4, Supermetrics, and Meta's attribution reporting. Where she gets thinner is in the structured explanation of how to reconcile conflicting attribution data and present it to a client in a way that drives a clear decision. When the topic came up in the interview, she addressed it through the push versus pull platform framing, which is a useful client-facing heuristic, but the more technical side of attribution modelling, such as how to handle multi-touch models or last-click versus data-driven differences, wasn't tested deeply and didn't come up in her answers unprompted.
• Developing a clearer personal framework for distinguishing metrics that need immediate action from those that are within normal variance would sharpen her optimisation decisions.