Building growth systems
across fintech, consumer, and AI

Built attribution and measurement infrastructure across millions of users spanning AppsFlyer, Adjust, Branch, Firebase Analytics, and Google Analytics. Expertise in multi-channel measurement, deep linking systems, deferred attribution, fraud prevention, cohort analysis, CAC optimization, lifecycle attribution, and performance measurement at massive scale. Strong understanding of attribution models in multi-touch environments and complex user journeys.

Deep expertise in Mixpanel, Amplitude, CleverTap Analytics, Firebase, and internal BI systems. Skilled in retention analysis, funnel diagnostics, cohort behavior mapping, event instrumentation, activation tracking, lifecycle optimization, user segmentation, and monetization analytics. Growth systems are driven by behavioral understanding—this infrastructure layer powers decisions at scale. Strong SQL and data infrastructure knowledge for building internal analytics systems.

Extensive experience with CleverTap, MoEngage, WebEngage, Firebase Messaging, and CRM systems. Built lifecycle automation systems spanning push, WhatsApp, email, and in-app messaging. Expert in onboarding journeys, repayment engagement, activation systems, retention mechanics, behavioral messaging, and engagement orchestration. Lifecycle systems become compounding infrastructure when aligned with user intent and product value—this is where retention meets economics.

Proficient in AppTweak, Sensor Tower, data.ai, AppFollow, SEMrush, and Ahrefs. Built ASO systems with deep keyword intelligence, category ranking optimization, conversion optimization frameworks, and competitive intelligence. Significant experience with search-driven acquisition and organic distribution as a long-term strategic advantage. Organic distribution compounds when treated as infrastructure, not tactics.

Managed large-scale acquisition across Google Ads, Meta Ads, Apple Search Ads, TikTok Ads, Reddit Ads, Outbrain, Amazon Ads, DV360, and programmatic ecosystems. Deep expertise in CAC optimization, creative testing frameworks, funnel economics, underwriting-aware acquisition strategies, performance scaling, incrementality measurement, and cross-channel optimization. This is not media buying—it's distribution systems tied to business economics, retention, and underwriting alignment. Managed ₹150+ Cr+ annual growth budgets across these channels.

Extensive experience architecting and scaling partnership-driven growth with India's largest ecosystems: Amazon, Flipkart, PhonePe, Swiggy, Zomato, Zepto, Blinkit, Moneycontrol, and Paytm. Expert in embedded distribution, contextual acquisition, embedded finance, and ecosystem-led onboarding. Understanding: distribution compounds fastest when products integrate naturally into existing user behavior. These partnerships represent infrastructure, not one-off campaigns.

Worked with 10+ affiliate and distribution ecosystems managing complex performance partnerships. Built partner quality systems, fraud control infrastructure, payout optimization frameworks, ROI measurement systems, and affiliate lifecycle management. Scaled performance partnerships from 0 to significant revenue contributors. Key insight: performance partnerships work as infrastructure when built with discipline and governance, not when treated as short-term arbitrage.

Built experimentation cultures focused on learning velocity over conversion optimizations. Expert in A/B testing systems, funnel experiments, onboarding optimization, behavioral testing, activation optimization, growth loops, referral systems, pricing experiments, and monetization testing. Worked cross-functionally with product, engineering, risk, analytics, underwriting, and lifecycle teams. The best experimentation cultures optimize learning velocity—this becomes your competitive advantage when embedded into organizational DNA.

Currently building AI-native operating systems through Vanikya.ai. Expertise in AI-assisted workflows, creative intelligence systems, AI-generated insights, workflow automation, multi-model AI architectures, and operator productivity systems. This represents the evolution from traditional growth stacks into AI-native infrastructure where humans provide strategy and judgment while AI handles execution, optimization, and learning cycles. The next generation of growth infrastructure is built on intelligence layers, not just data plumbing.