Data Engineer - Web Scraping in Madrid

StyleSage

Workplace
Onsite
Hours
Full-Time
Internship
No
Share offer

Job Description

YOUR JOB

As a web scraping focused engineer in the Data Engineering team, you will be responsible for ensuring that our data is always good quality, fresh and available by:
  • Maintaining our current spiders up to date.
  • Maintain and enhance our custom scraping framework based on scrapy.
  • Coordinate a team of "spidermen" to keep our spiders up to date.
  • Develop new spiders to continuously expand our data feed catalog.
  • Develop other data input feeds based on APIs and data files.

YOUR SKILLS

In order to maintain and create new pieces of the data pipeline, you will need to bring your skills and knowledge around these technologies:
  • Python, as it is the base of our tech stack at many levels.
  • Scrapy, you know why.
  • Good understanding of how the web works: requests, responses, user agents, proxies, HTTP protocol, robots.txt.
  • Linux shell command line, don't need to be a sysadmin but we expect you to be able to navigate yourself in your local machine and in a server box.
  • SQL, PostgreSQL, we work with data, you produce structured data.
  • English: half of the company doesn't speak Spanish and your job involves some written and spoken communication with people in other countries and timezones (and with different cultures by the way).

BONUS POINTS

You will learn on the job many of the other tech pieces we use. Of course it will be easier if you are already familiar with any them:
  • Docker
  • AWS
  • Node.js, puppeteer
  • JIRA
  • MongoDb

THE TEAM

Data Engineering team is responsible for collecting massive amounts of data from fashion related websites and placing it into our analytics data repository. There is a whole data pipeline that starts with hundreds of spiders (python/scrapyd) that continuously crawl websites, continues with a series of data quality and data enrichment processes (python, celery, rabbitMQ, SQL...) and ends dumping clean, validated and normalized product information for our customers to analyze. As an example, the pipeline includes data enriching with machine learned models and massively parallel processing using Spark. The pipeline consists of many pieces that need to be monitored, scaled up and down, maintained, deployed, enhanced and adapted to new business needs continuously. The database already contains more than 500 millions of products (refreshed daily) and we see 1-2M of new products every week.

THE COMPANY

Our motto is "We love data". And we love technology that deals with data because it enables us to do incredible things... things that are valuable for our customers and that sustain a business.

StyleSage is (no longer) a startup founded 5 years ago with offices in New York and Madrid. Madrid is the home for our core technical team of around 20 people. It's an open, diverse and inclusive team of very skilled and talented individuals that are happy to collaborate, share knowledge and enjoy building great software together. We are looking forward to welcoming additional members for this team.

WHAT WE OFFER

  • First and foremost: permanent contract and competitive salary.
  • Teams are made of people, not resources.
  • Open, diverse and inclusive environment.
  • A challenging and fun project to work and grow with, with the latest technologies, best practices and light speed evolution, all in a friendly, relaxed and positive environment.
  • Fixed yearly training budget to spend on english classes, courses, books, or conferences your brand new laptop with OS of your choice (we recommend MacOSX or any flavor of Linux).
  • A team of colleagues that will share a lot of knowledge with you (we have weekly in-depth internal talks).
  • A place to work in the center of Madrid (we have an office in a co-working space quite close to Avenida de América metro station).
  • Working hours are flexible and you can work remotely when needed, although you will miss the fruit and coffee/tea we stock in the office.
 

About StyleSage

  • Big Data

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