Data Engineer · Lahore, Pakistan · Open to senior roles
I'm Asad Ikram.I build the data systemscompanies actually rely on.
Chevening Scholar, MSc with Distinction, seven years across six countries. I've built pipelines and platforms for Amazon, Ford, Nike and the UK Government, and I run a data company of my own. Mostly I like the problems other people have given up on.
Chevening Scholar 2024/25 · top 2–3% of 70,000+ applicants · UK FCDO
Recognition · UK Foreign, Commonwealth & Development Office
Chevening Scholar 2024/25
The UK government's flagship global scholarship — one fully funded year, awarded on leadership and impact. It's how a scraping engineer from Lahore ended up publishing NLP research in Portsmouth.
Ten domains. Everything listed here has run in production for a paying client — no coursework, no weekend tutorials.
Experience
Where I've built things
Eight roles, six countries, one throughline. Tap any of them for the numbers.
Selected work
Systems I'm proud of
Six things I'd point to first — three products, an architecture that crawled daily for three years, a published field guide, and the scholarship that reframed the work.
Self-healing systems+
Ford Crawler IQ — Auto-Healer
A spider breaks at 3am. By the time anyone looks, an agent has already read the traceback, pulled the live page, tried four fix strategies, committed the one that worked to a path-locked branch and re-run the crawl. Seventy spiders, forty-four domains, nobody woken up.
Claude SonnetFastAPI MCPDjango DRFECS FargateAurora Serverless
Scope. ~70 Scrapy spiders across 44 car-forum domains, crawled weekly.
Guardrails. The agent can only touch spider files on a dedicated branch. It cannot reach pipelines, settings or workflows. Every fix arrives as a PR.
Limits. Hard 15-minute timeout at both loop and ECS task level, capped at three heal attempts per spider per week.
Escalation ladder. Every fetch walks the cheapest path first: curl_cffi → scrapling → webclaw → plain requests → camoufox → headless Playwright → headful cookie-warming. It stops at the first rung that returns a 200.
Cost. Migrated an overprovisioned RDS instance to Aurora Serverless v2, saving roughly $400/month, and 56/56 crawler tests pass on every deploy.
Where I'm CTO and co-founder. A Meta lakehouse, six fine-tuned NLP models, an agentic analyst chatbot and packaged B2B subscription datasets — with self-healing ops so it keeps running while I sleep.
One dashboard running a fleet of 86 crawler projects for a parts-retail client — schedule, watch, stop, validate and ship every crawl without touching a terminal. Seven users, zero SSH.
FastAPIAPSchedulerDrive syncCI/CD
Registry. 86 manufacturer crawlers with priorities, notes, audit log, soft-delete and restore — the catalogue is the source of truth for what runs when.
Drive as ground truth. Google Drive sync walks each crawler's output folder and uses the newest data file's timestamp as the real "last scraped" — no self-reported freshness.
Live ops. Colourised streaming logs, run ETAs from windowed completion rates, a real STOP button, and a smoke-test mode capped at 2 models / 25 items for safe dry-runs.
Zero-touch deploys. Push to main, GitHub Actions ships it to the VPS. The client team runs everything through named logins with per-action audit trails.
Ran daily for 3 years+
Dubizzle scraping architecture
500+ spiders on Kubernetes crawling every single day for three years straight across Dubizzle, Bayut, Zameen and OLX — feeding a three-layer warehouse, an Elasticsearch search tier and a competitor-intelligence product built partly from reverse-engineered mobile APIs.
EKSRedshiftElasticsearch
Fleet. 500+ regional scrapers on EKS with GitHub Actions CI/CD across Dubizzle, Bayut, Zameen and OLX.
Warehouse. Redesigned RDL → ODL → ADL for Propforce; throughput up 60%, directly tied to record revenue.
Reliability. Introduced unit testing and Scrapy contract validation, cutting production incidents 45%.
Recon. Reverse-engineered competitor mobile apps via Android Studio MITM to surface hidden REST and GraphQL APIs.
The field reference I wished existed — so I wrote it. 73 libraries, 10 anti-bot vendors mapped, 9 production architectures, and a full Akamai v3 case study with the failed attempts left in.
73 librariesAkamai v3JA4
Anti-bot map. Ten vendors — Akamai, Cloudflare, DataDome, Kasada, PerimeterX, F5 Shape and more — each with what they check and what actually beats them.
The honest case study. A production Akamai v3 bypass documented end to end, including every approach that failed before the one that worked.
Deep dives. JA3/JA4 fingerprinting, WASM SIMD detection, mobile API recon, fetch-ladder economics and a priority-ordered tooling cheat sheet.
Kept current. Search-indexed, continuously updated as the arms race moves — because a 2024 bypass guide is a history book.
Chevening isn't just funding — it's a leadership network of 160 countries and direct exposure to how the UK government thinks about data. It turned an engineer into a researcher.
UK FCDO160 countriesTop 2–3%
Selection. Chosen from 70,000+ applicants worldwide on leadership and impact — interviews, essays, references, the full gauntlet.
The research year. MSc at Portsmouth with Distinction — the dissertation put production scraping skills to academic use: 279k posts, a fine-tuned BERT at macro-F1 0.878.
The network. A global cohort of scholars across government, policy and industry — the kind of room a scraping engineer doesn't usually get invited into.
The result. UK client work at M+C Saatchi, a UK-registered company of my own, and PhD conversations that started from the dissertation.
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