Principal iOS SDK & Backend Developer (India)

Contemporary MMP's charges $2,000+/month and still cannot tell you which ad actually drove a paying subscriber. Their data is broken by Apple's ATT privacy framework, their SKAdNetwork implementation is a black box, and their influencer attribution is essentially guesswork. About Sira We are building Sira: a post-ATT attribution platform for iOS apps that is more accurate, more transparent, and built specifically for the era where IDFA is dead. We are looking for one exceptional engineer to build the core attribution engine from the ground up and make it genuinely better than what exists today. The Problem We Are Solving After Apple's ATT, every major MMP is struggling with the same core issue: they cannot deterministically match ad clicks to installs anymore. Their solution has been to bolt SKAdNetwork on top of their old architecture and call it a day. The result is coarse, delayed, and often wrong attribution data. The right solution is a new attribution framework built from scratch — one that treats SKAdNetwork as the primary signal, layers probabilistic fingerprint matching on top, and uses AI to fill the gaps where neither can reach. The goal is not to replicate AppsFlyer. It is to make them obsolete for iOS-first apps. What You Will Build iOS SDK (TrackKit — Swift Package Manager) - TrackKit.Init(apiKey:) on first app open — registers the device, captures anonymous fingerprint (IP, device model, OS version, screen resolution, timezone, locale), stores UUID in Keychain - SKAdNetwork.RegisterAppForAdNetworkAttribution() on install - StoreKit 2 transaction observer — fires purchase events to backend in real time - Deferred deep link capture for influencer campaign attribution - Drop-in simple: one line to initialize, zero configuration required Attribution Engine (Backend — Node.Js/TypeScript) - SKAdNetwork postback ingestion with Apple ECDSA signature verification - Probabilistic fingerprint matching: when a user installs the app, match their fingerprint against the click log within a configurable lookback window. Score the match probabilistically, not binary. - AI-assisted attribution layer: when SKAdNetwork and fingerprint matching both fail or conflict, use a lightweight model trained on historical install patterns to infer the most likely source. No MMP does this today. - Deduplication: one install is never attributed to two sources - Confidence scoring: every attribution result gets a confidence score (0–1) so customers know how reliable the data is Ad Platform Integrations - Meta Ads API — OAuth connect, pull campaign spend and impressions daily - Google UAC API — OAuth connect, pull campaign spend and installs daily - Apple App Store Connect API — pull revenue, proceeds, and unit sales Dashboard - React-based analytics dashboard showing ROAS, CPI, LTV, net profit, and channel breakdown — all wired to real attribution data You Must Have - Swift — you have shipped an iOS SDK or Swift Package before, not just an app - Node.Js + TypeScript — backend API development, event pipelines, database design - SKAdNetwork — you understand the postback flow, privacy thresholds, coarse/fine conversion values, and the 24–48 hour delay. If you have to Google what a postback sequence index is, this is not the right role. - Probabilistic matching — you have built or worked on a fingerprint or device-graph matching system - OAuth 2.0 — you have integrated at least one major ad platform API (Meta, Google, or TikTok) - Cryptography basics — JWT, ECDSA signature verification, webhook security Strong Advantage If You Have - Prior work at Adjust, AppsFlyer, Branch, Singular, Kochava, or a mobile growth agency - Experience with ML-assisted attribution or probabilistic inference systems - Familiarity with Apple Server Notifications and StoreKit 2 - A strong opinion on why current MMPs get post-ATT attribution wrong Structure - 1-month contract, - $3,500 fixed - Fully remote, async-first. - IST or EST timezone preferred - Long-term potential if the product gains traction, but no commitment beyond the initial month. How to Apply - Attach Resume - Alongside in the resume, add a Loom somewhere (you can add it in a link inside your resume) answering the following question: "SKAdNetwork 4.0 gives you a coarse conversion value of 'medium' and a 48-hour delayed postback. The ad network claims credit for the install. Your fingerprint match gives you 73% confidence it was organic. How do you attribute this install and what do you show the customer?"