Since day one at my current company, I’ve been on the Scraper Team. Recently, I was promoted to Scraper Tech Lead. I thought it would be a good time to write about what it means to be a scraper engineer, as well as the role and responsibilities of scraper engineers in a trending company.
Being a scraper engineer is like being a digital farmer in a world full of restaurant owners. While others focus on creating user experiences, we dig deep into the earth to extract the raw ingredients that fuel the digital economy. Let me break down what this unique role actually entails.
The Three-Headed Beast: BE + NE + DE#
A successful scraper engineer sits at the intersection of three crucial disciplines:
Backend Engineering#
We build a distributed crawler system that can be quickly deployed and scaled, including dozens of data pipelines with different functions and purposes, used to collect, process, and store data to support the company’s business needs and provide support for the company’s products.
Network Engineering#
Understanding the intricate dance of requests, responses, proxies, and CDNs is essential to our craft. We’re the people who know how to route traffic through different IPs, bypass rate limits, and navigate the complex web of network defenses that websites deploy specifically to stop us.
Data Engineering#
Most scraped data cannot be used directly and needs to be normalized and cleansed. In fact, scraping is a part of data engineering. We transform raw, messy data into structured information that provides real business value.
The Secret Sauce: Reverse Engineering (aka Hacking)#
This is where we truly shine. We don’t just accept APIs as they’re presented to us - we take them apart, figure out how they work, and then recreate them for our purposes. From decoding complex parameters to reverse-engineering authentication flows, we’re digital detectives solving puzzles that weren’t meant to be solved.
The Data Pipeline#
Once we’ve harvested the data, our job isn’t done. We’re also:
- Building robust data pipelines that can process information at scale
- Cleaning messy, inconsistent data from disparate sources
- Aggregating information to extract meaningful patterns
- Creating API services for data consumption
The Restaurant vs. Farmer Metaphor#
If backend and frontend engineers are hosting restaurants (creating experiences for users), then scraper engineers are the farmers who source the ingredients.
- Backend, Frontend → Host a restaurant
- Scraper → Farmer
Don’t ask a farmer how to improve customer experience - that’s not our domain. We’re the people who wake up early, get our hands dirty, and spend countless hours debugging to extract those precious data ingredients from the deep earth of the internet.
The Scraper Engineer’s Personality#
Embracing Chaos#
Nothing is stable in our world. Websites change without notice, APIs get protected with new measures overnight, and what worked yesterday might fail spectacularly today. A successful scraper engineer doesn’t fight this chaos - they embrace it as the natural state of their work.
Being Comfortable with Being Disliked#
Let’s be honest: the targets of our scraping efforts (the “scrapees”) don’t like what we do. We’re extracting value from their platforms, often against their express wishes. If you need to be universally loved, this isn’t the career for you.
Willing to Understand Others (Paradoxically)#
This seems to conflict with the previous point, but it’s actually complementary. To be effective at scraping, you need to understand how developers think, how they protect their data, and what they value. A wise person once said, “If you know people, they will like you; if you understand people too deeply, they will turn to hate you to protect themselves.”
This deep understanding of human nature is essential for anticipating how platforms will respond to your scraping efforts. For those interested in developing these personality traits, I recommend reading, in this order: «12 Rules for Life>, «The Courage to Be Disliked>, «The Laws of Human Nature».
Conclusion#
Being a scraper engineer isn’t glamorous. We don’t get to showcase beautiful UIs or talk about delightful user experiences. But we are the silent farmers providing the raw materials that power much of today’s data-driven world. It’s a career that demands technical excellence across multiple domains and a personality that can thrive in adversity. So the next time you use a price comparison tool or marvel at a comprehensive dataset, remember the scraper engineers who spent countless hours debugging network requests and reverse-engineering APIs to make it all possible.
FAQ#
- Do you use Python?
No. We use TypeScript. High-level scraping libraries like BeautifulSoup or Scrapy are good for many use cases, but as an enterprise, we develop lower-level scraping systems to make our operations much more efficient and maintainable.
- Isn’t scraping illegal?
It’s a gray area that depends on many factors. In our case, we primarily mimic human behavior on platforms, which keeps us in a safer territory.
- What’s the biggest challenge in scraping?
Change. Websites change constantly - sometimes deliberately to block scrapers. What worked perfectly yesterday might break today. This requires continuous monitoring, automated alerts, and rapid adaptation of our systems.
- How do you maintain scrapers long-term?
It’s a tricky part because of AB testing, regional rules, and other variables. We often need to maintain multiple format parsers at the same time. It’s a big challenge to keep the code clean and clear. We rely on a combination of good engineering practices and specialized techniques: robust error handling, comprehensive logging, automated monitoring, intelligent retries, and systems that can detect and adapt to minor changes automatically.
- Do you ever work with the companies you scrape from?
Actually, some of them are our customers, which I find quite interesting. They don’t want others to scrape their data, but they want scraping data from their competitors. Capitalism, huh?
- What’s the difference between a hobby scraper and an enterprise-level scraping operation?
In Taiwan, when someone starts learning scraping skills, the first practice would typically be scraping PTT (a popular Taiwanese forum). Its format changes seldom. But if you want to scrape some profitable platforms, they will change their format and policies all the time for better SEO, user experience, etc. So we need to continually adapt to these changes.
Also, as an enterprise use case, we need to consider scale, reliability, and maintainability. A hobby scraper might work for one-off projects, but enterprise operations need distributed infrastructure, monitoring, alerts, data validation, cleaning pipelines, error recovery, and systematic approaches to handling changes. We build systems, not scripts.
Acknowledgment: This article’s structure and language were refined with help from Claude AI (Anthropic). The content, experiences, and technical insights remain authentic to my work as a scraper engineer.
