PubPlus is a revenue attribution solution that helps publishers maximize content distribution spend, ad revenue, and user engagement.
PubPlus seamlessly aggregates publishers data into one easy-to-manage platform where ad revenue is attributed to the marketing spend which leverages valuable insights in order to maximize growth and profit.
Our mission is to empower publishers by providing them with insights most publications generally overlook. These insights allow them to make better business decisions and regain control of their full potential audience.
We are a rapidly growing start-up in the heart of Tel Aviv and are constantly looking for new, talented individuals to join our dynamic team.
We are looking for an experienced and enthusiastic back-end engineer to join our thriving R&D department.
The candidate will be joining a team with a unique DNA and take part in designing and building the data flows that run the PubPlus core. The ideal candidate is a team player that is passionate about highly distributed systems and analysis of large data-sets.
- Design, build and monitor the data collection, analysis and automation processes that drive the PubPlus core.
- Measure and improve business-oriented KPIs by designing and performing meaningful experiments based on analysis of constantly growing data-sets (billions of new records on a daily basis)
- Architect the underlying infrastructure with high availability, scalability and performance in mind
- Support the data science team in the design and implementation of automated model training, criticism, and comparison
- Proven experience designing, building, and operating distributed systems at large-scale
- Strong analytical and problem-solving skills
- Understanding of Web and API development (e.g HTTP specifications, REST APIs, caching mechanisms)
- Excellent technical communication with peers and non-technical cohorts
- B.S/M.S in computer science or equivalent field
- Experience with NoSQL databases (Redis is a plus)
- Proficiency in unit and integration testing paradigms
- Experience in Linux environments and shell scripting
- Familiarity with agile environments
- Experience with AWS and serverless environments
- Proficiency in Python programming for machine learning, with a good understanding of Pandas, NumPy, and scikit-learn
- Deep understanding of GoLang
- Experience with Node.js
- Understanding of Docker-based environments