Vetted full stack & backend engineers capable of delivering Analytics & Tracking solutions
Khmelnytskyi, Ukraine
Full-Stack Developer, Fractional CTO — Websites, Apps, Servers, Databases, AI, SEO & QA
Full-stack developer working across the entire stack — websites, apps, servers, databases, AI integrations, SEO, and QA. Languages & Core: writes code in JavaScript, TypeScript, Python, PHP, Go, and Rust. Architects scalable systems for large-scale projects. Frontend & Interfaces: builds websites and web applications with React and Next.js. Crafts responsive interfaces with Tailwind CSS, Radix UI and Shadcn, adds smooth animations with Framer Motion, and interactive charts with Recharts. SEO Audit & On-Page Optimization: semantic keyword research, resolving technical indexing issues, and optimizing page load speeds. Structures page architecture, meta tags, and multi-language support (i18n). Copywriting & Content Strategy: writes technical articles, drafts precise content briefs for writers, and develops content plans. QA & Testing: full-cycle testing for websites, web services, and Android apps — manual QA for UI/UX and business logic, plus automated testing with Jest, Vitest, Playwright and E2E. Backend, Cloud & Databases: complex API integrations of any scale. Builds servers with Node.js (Express, Fastify). Works with PostgreSQL, MySQL and MongoDB, ORMs (Prisma, Drizzle), and cloud infrastructure (Supabase, Firebase, Cloudflare). Browser Extensions & Automation: develops Manifest V3 browser extensions for Chrome, Edge, Firefox and other browsers. Builds web scrapers for complex data extraction using Puppeteer and Playwright. AI & Intelligent Agents: builds custom AI agents and integrates LLMs from OpenAI, Google Gemini, and Anthropic Claude via API, including Claude Code setups. Servers & DevOps: Linux (Ubuntu) and VPS administration — setup, updates, real-time monitoring, secure process isolation, Nginx, PM2, and CI/CD deployment via GitHub Actions. Telegram Bots: develops advanced Telegram bots (Telegraf, Grammy) integrated with AI, payment gateways, Google Sheets, and crypto exchanges. Desktop Applications: builds cross-platform software for Windows and macOS using Electron and Rust. Additional expertise: site development and customization with WordPress and Astro.
AI & Distributed Systems Architect | Enterprise AI, RAG, Cloud, Blockchain | Advisory & Fractional Leadership
AI and distributed systems architect providing advisory and fractional leadership on enterprise AI, RAG pipelines, cloud architecture and blockchain-backed data systems.
Khmelnytskyi, Ukraine
Senior Backend Engineer | AI Engineering | PHP, Python, LLM Applications
Senior backend engineer working across PHP and Python, with a growing focus on AI engineering: building LLM-powered application features on top of existing backend systems and APIs.
CTO & Founder at Punch AI | Building Software Harnesses for AI-Powered Development | PhD Student in Software Engineering
CTO and Founder at Punch AI, building software harnesses that make AI-powered development safer and more reliable. PhD student in Software Engineering, bridging applied AI engineering with academic research.
Digital marketing agencies routinely quote $150 to $250 per hour or demand $5,000 monthly retainers for tracking implementations. Within that billing structure, tracking tasks are typically delegated to junior media buyers or marketing coordinators who paste pre-built Google Tag Manager templates and toggle third-party plugin checkboxes. When ad platforms double-count revenue or Consent Mode v2 breaks tracking entirely, agency account executives schedule coordination calls instead of inspecting browser network payloads.
Working directly with an independent senior analytics engineer eliminates these communication layers. You collaborate directly with a specialist who understands JavaScript event loops, writes typed dataLayer pushes, deploys server-side containers, and reconciles conversion records directly with your system of record (Stripe, Shopify, or PostgreSQL). This direct model reduces delivery time by 60% and ensures your marketing spend is guided by verifiable data.
| Evaluation Factor | Digital Marketing Agency | ProofDevs Independent Senior Engineer |
|---|---|---|
| Hourly Rate | $150 – $250 / hr | $45 – $95 / hr |
| Implementation Method | Junior staff toggling plugin settings | Senior engineer writing typed dataLayer & server payloads |
| Verification Rigor | Surface-level green extension tag checks | End-to-end transaction traces reconciled with Stripe / DB |
| Server-Side & CAPI | Outsourced or billed as expensive add-on | Included: Server-side GTM, Stape, or GCP Cloud Run |
| Consent Mode v2 | Basic banner pasted without gap auditing | Verified Consent Mode v2 with quantified signal impact |
| Asset Ownership | Agency-managed GTM containers and credentials | 100% client-owned GTM, GA4, GCP, and Git repositories |
Client-side measurement architectures suffer severe degradation in Single Page Applications (SPAs) and frameworks like Next.js, Nuxt, and React. Standard pageview tags fire only during initial document parsing, missing subsequent client-side route transitions and creating fragmented session attribution. Furthermore, consumer ad blockers, Brave Browser shielding, and Apple Safari Intelligent Tracking Prevention (ITP) drop 20% to 40% of client-side tracking beacons, truncating cookie lifespans to 24 hours and starving ad platform optimization algorithms of conversion signals.
Server-side Google Tag Manager (sGTM) and Meta Conversions API (CAPI) resolve these visibility gaps by executing measurement on dedicated cloud infrastructure. Routing tracking beacons through a first-party subdomain (such as metrics.yourdomain.com) circumvents client-side ad-blocking rules and extends cookie persistence. However, executing a hybrid architecture (firing both browser pixels and server CAPI) introduces event duplication risks. If client and server events do not share identical, deterministic event_id parameters within a 48-hour deduplication window, ad platforms count transactions twice, distorting reported Return on Ad Spend (ROAS).
Regulatory enforcement surrounding European Digital Markets Act (DMA) compliance adds further architectural complexity. Google requires explicit ad_user_data and ad_personalization signals transmitted alongside analytics_storage under Consent Mode v2. Incorrect tag implementations frequently block all event execution when users decline cookies, eliminating aggregated traffic metrics. A senior analytics engineer configures advanced Consent Mode v2 to transmit cookieless pings that preserve machine learning modeling without violating user consent preferences.
Senior analytics engineers build measurement systems based on formal event schemas, declarative tag managers, and cloud compute environments. The standard toolchain incorporates Google Tag Manager (Web and Server containers), Google Analytics 4, Meta Conversions API, Google Ads Enhanced Conversions, and BigQuery for raw event streaming. Incompetent developers frequently attach triggers to volatile DOM elements like CSS classes or button text. When a designer updates button copy from 'Buy Now' to 'Complete Purchase', tracking triggers silently break, halting conversion reporting without throwing console errors.
Qualified specialists enforce dataLayer architectures where backend servers and frontend frameworks push structured JSON payloads directly to window.dataLayer. Triggers fire exclusively on explicit custom event names carrying standardized ecommerce schemas (view_item, add_to_cart, purchase), shielding measurement pipelines from visual frontend redesigns.
Architecture Rule: Enforce Strict Event Deduplication Across Browser & Server
When executing hybrid tracking (browser pixel + server-side CAPI), every conversion event must pass an identical, uniquely generated event_id from both client and server within a 48-hour deduplication window. Failing to match event_id parameters causes Meta and Google Ads to register identical orders twice, artificially inflating reported return on ad spend (ROAS) and destabilizing automated bidding algorithms.
Analytics engagements range from urgent discrepancy triage to complete enterprise server-side measurement setups. Documenting realistic engineering hour allocations allows marketing directors and founders to forecast project costs and delivery milestones accurately.
Turnaround speed depends on site architecture, checkout platform constraints, and whether custom backend API integrations are required. A senior engineer breaks implementation down into verifiable phases.
Independent analytics engineers on ProofDevs set transparent hourly rates between $45 and $95 per hour based on server-side architecture experience, dataLayer complexity, and data warehouse proficiency. Compared to marketing agencies billing $150 to $250 per hour, this direct model delivers a 60% reduction in development costs.
The primary return on investment in analytics engineering lies in media spend efficiency. When a business spends $15,000 to $100,000 monthly on paid acquisition, a 25% tracking blind spot misallocates thousands of dollars every week. Incomplete conversion signals cause smart-bidding algorithms in Google Ads and Meta to optimize toward low-intent traffic. An investment of $1,500 to $3,500 in clean tracking and server-side CAPI pays for itself immediately through improved bidding efficiency and reduced customer acquisition costs.
Clients retain 100% legal ownership of all Google Tag Manager containers, GA4 properties, cloud server configurations, and documentation upon milestone completion. You are never tied to agency proxy accounts or forced into recurring proprietary software retainers.
Four architectural patterns govern web measurement: pure client-side tagging via browser pixels, hybrid tracking pairing client pixels with server-side APIs, pure server-side tracking eliminating third-party browser scripts, and direct webhook-to-API data pipelines. Pure client-side tracking requires zero cloud hosting fees but sacrifices up to 40% of conversion signals to browser ad blockers.
Hybrid tracking provides the industry standard for ecommerce and performance marketing, capturing client-side browser context while backfilling missing conversions through server-to-server APIs with automated deduplication.
| Architecture Pattern | Signal Retention Rate | Browser Latency Impact | Monthly Hosting Cost | Recommended Application |
|---|---|---|---|---|
| Client-Side Pixel / Web GTM | 60% – 75% (heavy ad-blocker loss) | High (multiple 3rd party JS scripts) | $0 / month (free tier) | Early-stage blogs, informational sites with zero paid ad spend |
| Hybrid Tracking (Browser + CAPI) | 92% – 98% (deduplicated) | Moderate (single GTM container) | $10 – $35 / month (Cloud Run / Stape) | Ecommerce brands and lead-gen businesses running paid media ($5k+/mo) |
| Pure Server-Side (sGTM Only) | 98% – 99% (zero browser script) | Lowest (zero 3rd party client JS) | $25 – $80 / month (Cloud Run / AWS) | High-security SaaS, fintech, healthcare sites requiring complete data governance |
| Direct Webhook / API Pipeline | 99% (backend-to-backend) | Zero (executed asynchronously) | $0 – $15 / month (worker queues) | Subscription platforms reconciling Stripe payment webhooks directly into ad APIs |
Websites that have operated for several years frequently accumulate tracking debris: deprecated Universal Analytics (analytics.js) snippets, duplicate Meta Pixels installed across competing plugins, and inline tracking code scattered through theme files. This tag sprawl bloats page weight, degrades Core Web Vitals, and fires duplicate conversion events that distort business intelligence reports.
A senior analytics engineer executes migrations systematically without disrupting live reporting baselines. They conduct an inventory of all active network requests, audit plugin triggers, construct a single versioned Google Tag Manager container, and replace hardcoded theme scripts with formal dataLayer events. Before deactivating legacy plugins, the engineer runs parallel verification to ensure event counts match prior baselines before finalizing the cutover.
Before modifying an inherited tracking setup, a senior analytics engineer performs a detailed pre-hire audit. In 2 to 4 hours, the engineer reviews GTM container version history, inspects active tag triggers, verifies event deduplication parameters, evaluates Consent Mode behavior, and reconciles GA4 purchase counts against Stripe or backend database records.
A complete container rebuild is recommended only when an existing setup contains hundreds of undocumented tags, circular trigger dependencies, or unversioned manual edits that expose user passwords or PII. In standard scenarios, a senior engineer cleans up obsolete triggers, fixes variable references, and restores measurement integrity within 8 to 16 hours.
Audit Rule: Inspect Network Payloads in DevTools, Not Plugin Dashboards
Never trust green checkmarks inside WordPress plugins or third-party tag validator extensions. A senior analytics engineer inspects raw HTTP network requests in browser DevTools (collect endpoints for GA4, tr endpoints for Meta Pixel), verifying query parameters (en, ep.*, event_id), payload status codes, and server response headers under live user transactions.
Senior analytics engineers distinguish themselves during technical evaluations: they explain how first-party cookies interact with ITP, demonstrate SHA-256 data hashing for Enhanced Conversions, and explain the mathematical difference between attribution models and conversion tracking bugs. Incompetent candidates rely on fragile click-text triggers that break during frontend redesigns, fail to generate deterministic event_id values, or confuse GA4 session metrics with backend transaction records.
A paid 3-to-5-hour practical trial provides conclusive evidence of technical capability. Provide the candidate with staging website access and request the implementation of an ecommerce purchase event via dataLayer, verification of Consent Mode v2 triggers, and the production of a reconciliation report matching test orders against simulated payment gateway payloads.
You will receive matches with two to four available senior analytics engineers within 2 to 4 hours of submitting your website URL, stack details, and tracking requirements.
Hourly rates range from $45 to $95 per hour, set directly by each independent engineer based on their expertise in server-side GTM, Meta Conversions API (CAPI), Consent Mode v2, and dataLayer engineering.
Yes. Many projects on ProofDevs are single-task engagements, such as diagnosing duplicate purchase tags, auditing tracking discrepancies between Meta and Stripe, or configuring Consent Mode v2.
Discrepancies typically stem from duplicate client-side triggers, ad-blocker signal loss, cross-device attribution windows, timezone offsets, or missing server-side deduplication. A discrepancy audit isolates each divergence to its exact trigger and network payload.
Server-side tracking is recommended if you spend more than $5,000 monthly on paid media, operate an ecommerce store losing signals to Safari ITP, or handle sensitive user data that requires client-side PII redaction before transmission.
Both the browser Meta Pixel and server CAPI must transmit identical event_name and event_id values for each transaction; Meta matches these keys within a 48-hour window to count the conversion once while merging signal data.
The audit includes a complete dataLayer review, duplicate-tag inventory, Consent Mode verification, platform-by-platform reconciliation against Stripe or your database, and an actionable remediation roadmap.
Consent Mode v2 adjusts tag behavior based on user cookie preferences; when configured correctly, it transmits cookieless pings that preserve machine learning attribution in Google Ads while remaining compliant with European DMA regulations.
A standard web tracking setup takes 8 to 18 engineering hours; a discrepancy audit takes 4 to 12 hours; server-side CAPI deployment takes 15 to 30 hours; and a full enterprise ecommerce measurement pipeline takes 25 to 50 hours.
You retain 100% legal ownership of all Google Tag Manager containers, GA4 properties, cloud server instances (GCP/AWS), and documentation upon milestone completion with zero platform lock-in.
Provide standard collaborator access to your Google Tag Manager container, Google Analytics 4 property, ad platform accounts (Google Ads, Meta Events Manager), and staging website code or CMS admin.
Engineers perform live test purchases, trace HTTP network payloads in browser DevTools, confirm server-to-server CAPI delivery in platform event debuggers, and reconcile final numbers against Stripe or order database records.
