Data Intelligence Engineer

Description

Freelance Data Intelligence

Length - 3 months

Project description
In order to digitally transform its asset and maintenance management, the Group is implementing the "Asset Automation & Digitalisation" (AAD) programme. Important topics of this programme are intelligent vehicles, the central collection and analysis of data and the automation of related processes. The Asset Intelligence Centre (AIC) is an essential core of the AAD programme.
The AIC is the central data and analysis platform that provides processed vehicle condition data for analyses and live use in operational processes (use cases). A prerequisite for the effective and efficient functioning
of this data and analysis platform is a stringent overall architecture. It describes the basic structures and principles for the data and information processing of the AAD programme, their construction, use and further development.

# Performance description
- Analysis of structured and semi-structured data sources and support for data analysis and data evaluation
- Design of data models and architecture solutions for the AIC
- Implementation of ETL pipelines based on various Big Data applications (mainly Apache Spark, Hive, Impala).
- Creation of Tableau dashboards
- Consulting in the area of Data Science/Data Engineering
- Professional and technical consulting in the area of programming analyses at the interface to Condition Based Maintenance (CBM), Digital Fleet Control (DFS) and incident management in the Asset Intelligence Centre (AIC)
- Advising the AIC development team on data intelligence engineer services
- Documentation of work and support services in Confluence. Documentation of operational and development (DevOps) performance in JIRA.


# Must-Requirements:
- Several years of experience in Big Data and IoT development as well as knowledge in AI and Machine Learning (especially with technologies such as: Python, SQL, Java, JavaScript, AWS, OPC-UA, MQTT, Jupyter).
- Proven experience with project management tools, such as Confluence and Jira
- In-depth experience of working with complex software architectures
- Experience in software migration
- Experience in ETL
- Experience in agile software development
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