Open to hire in the following Job locations: Luxembourg

Do you want to be in the forefront of engineering big data solutions that takes Transportation models to the next generation? Do you have a solid analytical thinking, metrics driven decision making and want to solve problems with solutions that will meet the growing worldwide need? We are looking for Data Engineers to be part of our world class Transportation Business Intelligence team. We are building real time analytical platforms using big data tools and AWS technologies like Hadoop, Spark, EMR, SNS, SQS, Lambda, Kinesis Firehose, DynamoDB Streams.

The ideal candidate relishes working with large volumes of data, enjoys the challenge of highly complex technical contexts, and, above all else, is passionate about data and analytics. An expert with data modeling, ETL design and business intelligence tools and passionately partners with the business to identify strategic opportunities where improvements in data infrastructure creates out-sized business impact. The ideal candidate is self-starter, comfortable with ambiguity, able to think big (while paying careful attention to detail), and enjoys working in a fast-paced and global team. They should also have expertise in data engineering, SQL experience, as well as experience with data modeling, warehousing, and building ETL pipelines and proficiency with at least one of the contemporary scripting or programming languages, including NodeJS, Java, Scala, or Python.

We're also looking for someone with preferred experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions. Experience working independently on and completing end-to-end projects. Track record of managing other ERP systems, with a deep knowledge of best practices within the system for sales, invoicing, reporting, etc. Knowledge of professional software engineering and best practices for the full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence




A day in the life
- Create, design, build, test, document, and manage massively parallel, high-throughput, high-performance data structures for AMZL analytics and business intelligence.
- Use best practices for data modeling, ETL/ELT procedures, SQL, Redshift, and OLAP technologies to implement data structures.
- Collect and convert functional and business requirements into solutions that are operable, scalable, and well-suited to the overall data
- Determine best practices for creating data lineage from a range of data sources by analyzing source data systems. data sources.
- Engage in all phases of the development life cycle, including design, implementation, testing, delivery, documentation, support, and maintenance.
- Generate complete, reusable metadata and dataset documentation.
- Assess and decide on the implementation of datasets created and suggested by other data engineers. - Experience in data engineering
- Experience with data modeling, warehousing and building ETL pipelines- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
- Knowledge of batch and streaming data architectures like Kafka, Kinesis, Flink, Storm, Beam

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