About Geoblink
We’re a fast growing startup that has already raised close to $8 million in investment from leading venture capital firms, and have been named by Bloomberg as one of the 50 most promising startups in the world to look out for. Our goal is to revolutionise the world of Location Intelligence and the way businesses think about, and act upon location intelligence data.
At Geoblink we use the latest technologies to find solutions to real world problems businesses face when trying to expand or increase efficiency. We leverage GIS technologies and Big Data to create a beautiful map-based user interface that not only provides lots of awesome statistics but also a great user experience.
We are proud of the environment of collaboration and diversity we have built and continue to foster, with plenty of opportunities to have a real impact on the business.
About Geoblink Tech
Our systems are built using an SOA approach that allows us to perform multiple deployments per day. We <3 monitoring, pull requests, iteration, continuous deployment and automated testing. The trunk of our stack is Python, Node.js, Vue.js, PostgreSQL and Spark but our architecture is language-agnostic. We move fast but put a lot of thought into the design of our architecture so that it’s simple and scalable. We write clean, modular code to produce great software that solves the needs of our clients.
Our Tech&Data culture is based on the high standards we try to achieve in everything we build and the personal development of our team. We foster an inclusive atmosphere of non-ego and respect where ideas are shared and feedback is used to promote quality and innovation. Some initiatives we have in place are hackathons twice a year, bi-weekly Tech&Data talks, personal development budget for books, training and conferences and time for side projects every other Friday.
You can visit our Tech blog to learn more about the projects and technologies at Geoblink.
About the POI-Acquisition team
Data is at the heart of all the technical challenges at Geoblink. The POI-Acquisition team is part of our Data department and is dedicated to mining, normalizing and processing points of interest that are a key part of the product features of our solution. This includes a set of very interesting (and complex!) tasks like geocoding, string normalization through machine learning, deduplicating, etc. To be able to increase the volume of information we are able to process, we continuously think about new ways to automate these processes at different levels (raw data processing, data pipelines, internal tooling, etc). This requires a mix of different roles in the team: Data Scientists for process ideation and analysis, Data Engineers to implement some of the most complex parts of the data treatment, and Software Engineers to help with parts of the implementation and plug the results into our testing and production systems, including back end and front end.
About the job
You would join the POI-Acquisition team as a Software Engineer, working with the Data Scientists to acquire and process hundreds of POIs on a daily basis (and more as we keep growing!). This will involve tasks to develop the back end (Node.js and Python) of different applications (back end services and internal web apps) to read data from databases or CSV files and process it in different steps, creating automated pipelines when possible.
This said, the collaboration between team members will be very close, so expect to be involved in Data Scientist tasks to fully understand the requirements of the projects, design the right approaches for the problems and help in different areas of the implementation.
Who we’re looking for
The salary bracket for this position is 35.000€ to 45.000€ a year.
Perks of the job
We have something called the “zero-policy” which means there are no restrictions on vacation days, office hours, working from home days, etc. We believe everyone here is a “mini-CEO”, and should have the flexibility and the opportunity to make their own decisions about their work schedule.
Other perks we offer: