PPC Expert

Compensation: ₹8L – ₹12L

Required Skills: Senior Paid Search Lead Location: [Remote] Type: [Full-Time]

About Us: We are a performance-driven search marketing team that manages high-spend client accounts across Google Ads, Microsoft Advertising, and regional search engines. We are judged on one thing: profitable, measurable business growth for our clients. Our work is built on first-party data, server-side measurement, value-based bidding, and incrementality testing, not on platform-reported vanity metrics. We give our senior people full ownership of client accounts and hold them accountable for real revenue, profit, and pipeline outcomes. Role Overview: We are looking for a Senior Paid Search Lead who can take a complete client account and grow it independently, with no strategist, analyst, or manager above them on that account. You will own everything from the initial audit and tracking architecture to bidding strategy, Performance Max control, feed optimization, experimentation, and direct reporting to the client’s CMO or founder. This role is built for someone who has already personally scaled accounts spending $100,000+ per month, can prove it with before-and-after numbers, and is as comfortable writing Google Ads Scripts and SQL queries as they are defending a growth plan in a boardroom. If your experience is mainly in social media buying, campaign execution under someone else’s strategy, or results you can only describe in CTR and impressions, this role is not a fit.

1. Bidding and Algorithm Control Value-based bidding architecture: designing conversion value schemes using margin, predicted LTV, or lead-stage values, and applying conversion value rules (by location, device, audience) to steer tROAS toward profit rather than revenue. Marginal vs. average ROAS thinking: using Performance Planner, bid strategy simulators, and spend-response curves to find where the next dollar stops being profitable, rather than optimizing to a blended ROAS target. Learning-phase management: knowing what resets learning, when to use seasonality adjustments for short predictable spikes (sales, launches), and when to apply data exclusions after tracking outages. Portfolio bid strategies with max/min CPC bid limits, shared budgets, and target impression share used deliberately for brand defense. New customer acquisition goals: configuring “bid higher for new customers” or “new customers only” modes using Customer Match lists and conversion-based customer detection.
2. Performance Max at Expert Level Cannibalization control: account-level brand exclusions, account-level negative keywords, and final URL expansion rules to stop PMax from harvesting brand and existing-customer demand. Asset group segmentation by margin tier, product category, or audience signal, with feed-only or retail-focused PMax setups where creative quality is weak. Channel performance and search term insights analysis to determine where spend actually goes (Search, Shopping, YouTube, Display, Discover, Gmail, Maps) and act on it. PMax vs. Standard Shopping vs. Search testing using campaign experiments and the ability to explain overlap and priority rules between them.
3. Search Campaign Craft N-gram analysis of search term reports at scale to find wasted spend patterns and new themes, rather than reviewing terms line by line. Match type strategy in the AI era: knowing when broad match plus Smart Bidding works, when it burns money, and how AI Max for Search changes query matching and final URLs. Negative keyword architecture: shared lists, cross-campaign sculpting, and protection against keyword conflicts. RSA asset strategy: pinning only where necessary, writing assets that each stand alone, and reading asset-level performance data correctly.
4. Measurement and Tracking Engineering Server-side Google Tag Manager deployment with a first-party subdomain, including understanding of data flow, cookie lifetime benefits, and cost implications. Enhanced Conversions (web and for leads) with correctly hashed first-party data, plus diagnosing match rate issues. Consent Mode v2 in advanced mode with a certified CMP, and understanding how conversion modeling fills consent gaps. Offline conversion imports using GCLID, GBRAID, and WBRAID, automated through CRM integrations (Salesforce, HubSpot, Zoho) or the API, including conversion adjustments (restatements and retractions) for refunds and cancellations. GA4 to BigQuery export and the ability to reconcile platform-reported conversions against backend revenue. Conversion action hierarchy design: primary vs. secondary actions, conversion goals at account and campaign level, and counting settings.
5. Incrementality and Advanced Measurement Geo-based holdout experiments (matched markets, GeoLift or time-based regression methods) with power calculations and minimum detectable effect set before launch. Conversion lift studies on YouTube and Demand Gen, and knowing their limitations. Marketing mix modeling literacy: able to work with outputs from tools like Google Meridian or Robyn and translate them into budget decisions. Statistical rigor in testing: sample size, test duration, significance, and avoiding peeking and novelty effects.
6. Shopping and Feed Engineering Merchant Center Next and feed rules: supplemental feeds via Google Sheets or API, attribute rewriting, and fixing disapprovals and account-level suspensions (misrepresentation, policy). Custom label strategy driven by margin, stock levels, price competitiveness, and bestseller status, refreshed automatically. Title and product type optimization based on search query data, not guesswork. Price competitiveness and bestseller reports used to inform bidding and merchandising conversations with the client. Feed management platforms such as DataFeedWatch, Feedonomics, Channable, or Producthero.
7. Automation and Data Engineering Google Ads Scripts (JavaScript) written personally: budget pacing, anomaly alerts, n-gram reports, broken link checks, and automated negative keyword mining. Google Ads Query Language (GAQL) and working knowledge of the Google Ads API. SQL in BigQuery, using the Google Ads Data Transfer Service to build custom reporting tables. Looker Studio or similar BI tools for live client dashboards blending ad data with CRM and backend revenue. Python for analysis and automation is a strong plus.
8. Microsoft Advertising Beyond the Import Button Managing Microsoft-specific features: LinkedIn profile targeting for B2B, Microsoft Audience Network, Microsoft PMax, and Copilot-adjacent ad placements. Adjusting imported campaigns for Microsoft’s different auction dynamics, audience size, and bid levels instead of mirroring Google settings. Running separate UET tracking and Microsoft offline conversion imports.
9. Commercial and Financial Acumen Unit economics: contribution margin, POAS (profit on ad spend), LTV:CAC, payback period, and break-even ROAS calculations. Forecasting: building spend-to-revenue forecasts and growth scenarios the client’s finance team can sign off on. Budget negotiation: making a data-backed case for budget increases or reallocation to client leadership. Lead gen economics: cost per SQL, cost per opportunity, and pipeline-to-revenue modeling.
10. Audience and First-Party Data Strategy Customer Match segmentation by value tier, recency, and lifecycle stage, with regular automated refreshes. Exclusion strategy for existing customers, low-value segments, and converters. Audience signals for PMax and Demand Gen built from first-party data rather than generic in-market segments.
11. Account Diagnostics and Problem-Solving Root cause analysis of performance drops by separating tracking, auction, demand, competitive, site, and seasonality factors. Auction Insights trend analysis to detect competitor entry, aggressive bidding, or brand hijacking, and handling trademark complaints. Policy and suspension recovery: advertiser verification, circumvention or misrepresentation suspensions, and industry certifications (financial services, healthcare, gambling where applicable). Landing page diagnosis: identifying CRO issues hurting paid performance and briefing developers or designers on specific fixes.
12. Client Ownership Skills Running quarterly business reviews and strategy sessions directly with CMOs, founders, or marketing heads. Pushing back on clients with data when their requests would hurt performance. Writing clear growth plans with prioritized initiatives, expected impact, and timelines. Minimum Requirements (Non-Negotiable) 6+ years of hands-on paid search, with at least 3 years as the primary owner of accounts (not support, not “assisted with”). Personally managed a single account spending at least $100,000/month (₹80 lakh+), or a portfolio of $3M+ annually. A documented growth outcome you led, stated in numbers: for example, scaled revenue 2x+ at stable or improved ROAS, or cut CPA 30%+ while growing volume. Your resume must state the before and after figures, timeframe, and spend level. Full ownership of conversion tracking: you have personally implemented GTM (including server-side), GA4, Enhanced Conversions, Consent Mode v2, and offline conversion imports from a CRM. “Worked with the dev team” does not count. Value-based bidding in production: you have fed margin, LTV, or lead-quality values back into Smart Bidding and can show the impact. Performance Max at scale with control: brand exclusions, asset group segmentation by margin or product tier, feed-level custom labels, and search term/placement analysis. Microsoft Advertising experience beyond copying Google campaigns via import. SQL and scripting: you have written Google Ads Scripts or used the Google Ads API, and can query data in BigQuery or similar. Direct client-facing ownership: you have presented strategy and results to client decision-makers (Head of Marketing, CMO, founder) without a manager in the room. Strongly Preferred Incrementality testing experience (geo-holdouts, conversion lift studies) and a working understanding of MMM. Merchant Center feed optimization for catalogs of 5,000+ SKUs. Multi-market or multi-language account experience. Search Ads 360 experience. Experience rescuing an underperforming account and turning it around.