Apr 2022 – Present · 4 yrs 2 mos

• 1) Tech for Giftables Business:
• Led tech design and execution for Swiggy’s Giftables BL across cart, order, post-order and comms, enabling 5,000+ SKUs, complex refund/replacement flows, and GTM-ready, reusable foundations for future gifting journeys. Partnered closely with product and multiple engineering pods to define scope, simplify flows across Food/IM/Gifting, and drive decisions on order hierarchy, collections, and refunds. Mentored next-level ICs, set up structured execution (contracts, CFDs, dashboards), and ensured quality, opex readiness and SOPs for a high-complexity, multi-quarter launch.
• 2) Org Tech Simplification:
• Drove org-wide tech simplification across Membership, Config Platform and Communication Platform, deprecating high-cost DUs/APIs, cutting infra spend by $35K/month, and improving latency, reliability and self-serve opex for teams. Led Loyalty (Shaktimaan) modernization and call-depth reduction, Redis/storage and CPU optimisations, and membership-context based traffic reduction (1.5 million TPM/RPM) to unlock both performance and cost wins. Re-architected Config Platform governance and SDK, simplified push-notification architecture, and institutionalised data-backed simplification roadmaps, enabling safer rollouts and easier platform adoption across the org.
• 3) Tech for Ads Domain:
• Built Swiggy’s in-house Ads platform end-to-end, scaling multi-BL Sponsored/Keyword/Banner Ads to multi-million RPO while saving ₹40L/month and leading migration from legacy systems. Drove DSP/SSP, post-serving, and advertiser metrics design, shaped contracts and policies with BLs, and established clear boundaries between Ads and business domains. Enabled rapid onboarding for new BLs, delivered high-RPO use-cases like Category Sponsored Ads and keyword bidding, and ensured production readiness through performance, opex, and GTM processes.

Jan 2020 – Mar 2022 · 2 yrs 2 mos

• 1) Designed and owned core Ads systems:
• (Ads Bidder, Ads Event Consumers, Ads Serving Layer), enabling CLM-based audience segmentation across 12+ segments with zero incremental tech effort for new segments. Built robust observability, debuggability and schema foundations so product, DS and analytics teams could reliably trace pacing, targeting and DSP responses end-to-end.[1]
• 2) Drove execution of complex cross-team projects:
• (Ads Re-Arch, Similar Restaurants, CPM/BYO) involving 4–8 developers and multiple systems, defining rollout, parity and production-readiness plans. Improved Ads Bidder latency by ~38% via targeted perf testing and tuning (e.g., GOGC and memory profiling), and unblocked launches by resolving bottlenecks across Ads, Search, DS, Merch and Foundations while keeping GTM timelines intact.[1]
• 3) Reduced operational load and infra cos:
• By cutting Ads pricing upload effort from ~4 days of manual ops to ~12–16 hours through deep-dives across dashboard, consumers and RDS, and by fixing RDS CPU spikes via batching, rate limiting and sequence optimizations. Eliminated Kafka lag in high-traffic scenarios through parallel consumption (goroutine pool per node), and pushed higher UT/SLT coverage plus whitewalker adoption to make new features more resilient and safer to roll out.