Position: Data Engineer
Company: GeeksForLess
Experience required: 3y
Category: Data Science
English level: upper
Published: 2023-06-01T00:00:00+03:00

Location: Amsterdam, Hybrid ( work form office once a week) 
Working schedule: 09.00-18.00

**Requirements:** 
- We are looking for a Data Engineer who enjoys solving problems, who initiates solutions and discussions and who believes that any challenge can be scaled with the right mindset and tools.
We have found that people who match the following requirements are the ones who fit us best:
- Minimum of 3 years of experience in the field, using 2 or more server-side programming languages -- preferably Java, Scala, Python, Perl, etc.
- Experience with building scalable data pipelines in distributed environments with technologies such as Hadoop, Cassandra, Kafka, Spark, HBase, MySQL, etc.
- Knowledgeable about data modelling, data access, and data storage techniques.
- Understands and can develop streaming processing applications using technologies like Flink, Kafka-Streams, Spark-Streaming, etc.
- Hands-on experience of developing in and contributing to open-source data technologies, such as Hadoop.
- Demonstrable experience with SQL, HQL, CQL, etc.
- Experience of working on systems on a large scale.
- Good understanding of basic analytics and machine learning concepts.
- Preferably a university degree in Computer Science.
- Excellent communication, written and spoken.

**Responsibilities:**
- As a Data Engineer you are responsible for the modernisation and scalability of accommodation reservation backend solutions. You will work independently and will also be responsible for making technical decisions within a team, in close cooperation with the team and other stakeholders.
Important aspects of the job include:
- Rapidly developing next-generation scalable, flexible, and high-performance data pipelines.
- Solving issues with data and data pipelines, prioritising based on customer impact.
- End-to-end ownership of data quality in our core datasets and data pipelines.
- Experimenting with new tools and technologies to meet business requirements regarding performance, scaling, and data quality.