16.11.2025 aktualisiert
100 % verfügbar
Data Scientist / Data Engineer
Wendeburg (Neubrück), Deutschland
Deutschland
M.Sc. Mechanical and Process EngineeringSkills
SAP CloudLuftfahrtAmazon Web ServicesAmazon S3ConfluenceJiraAutomobilindustrieMicrosoft AzureBankingTelekommunikationBusiness IntelligenceCloud ComputingCloud DatabaseCloud-EngineeringDatenbankenInformation EngineeringETLPythonPostgresqlMatlabMachine LearningMysqlScrumPower BiAzure Machine LearningSap HanaAmazon Simple Notification Service (SNS)SQLTableauZeitreihenanalyseJupyter NotebookGoogle CloudData ScienceAzure Data FactoryGitCloudformationAmazon RdsPysparkGoogle BigquerySplunkProgramming Languages
Business Intelligence:
Tableau, Splunk Enterprise, Microsoft Power BI, Quicksight
Cloud Technologies:
AWS, Azure, Google Cloud, SAP
Data Science:
Machine Learning, Time Series Analysis
Databases:
(Aurora) PostgreSQL, MySQL, Google BigQuery, SAP HANA
IDE:
Spyder, Pycharm, Jupyter Notebook, VSCode
IT Development:
Git, AWS CloudFormation, Terraform
Methods:
Atlassian (Confluence, Jira), SAFe, SCRUM
Programming languages/Frameworks:
Python, PySpark, Matlab, SQL, Apache (Spark, Iceberg)
Industries:
Mechanics, Automotive, Aviation, Telecommunication, Banking, Crowdsourcing
Tableau, Splunk Enterprise, Microsoft Power BI, Quicksight
Cloud Technologies:
AWS, Azure, Google Cloud, SAP
Data Science:
Machine Learning, Time Series Analysis
Databases:
(Aurora) PostgreSQL, MySQL, Google BigQuery, SAP HANA
IDE:
Spyder, Pycharm, Jupyter Notebook, VSCode
IT Development:
Git, AWS CloudFormation, Terraform
Methods:
Atlassian (Confluence, Jira), SAFe, SCRUM
Programming languages/Frameworks:
Python, PySpark, Matlab, SQL, Apache (Spark, Iceberg)
Industries:
Mechanics, Automotive, Aviation, Telecommunication, Banking, Crowdsourcing
Sprachen
DeutschMutterspracheEnglischverhandlungssicherFranzösischMuttersprache
Projekthistorie
Project description
The automotive company has a monitoring and reporting team, which is a central enabler for
different stakeholders (model series, development, quality, sales), facilitating data-driven
decisions and presenting customer experiences. Monitoring includes tracking and evaluating the
performance, availability and reliability of a connect service from the user's perspective.
Objective of this project is to migrate the existing SAP application to AWS in order to have more
flexibility, better performance, cost optimization and more options to monitor the application.
Tasks
The automotive company has a monitoring and reporting team, which is a central enabler for
different stakeholders (model series, development, quality, sales), facilitating data-driven
decisions and presenting customer experiences. Monitoring includes tracking and evaluating the
performance, availability and reliability of a connect service from the user's perspective.
Objective of this project is to migrate the existing SAP application to AWS in order to have more
flexibility, better performance, cost optimization and more options to monitor the application.
Tasks
- Development and implementation of ETL pipelines in AWS for the integration and processing of vehicle data
- Analysis, optimization and expansion of existing data pipelines in SAP
- Cost analysis and forecast of AWS infrastructure
- Conceptual design and implementation of a role concept in AWS IAM
- ETL workflow monitoring
- Create and work on User Stories and participation in agile meetings according to SAFe
- Documentation
Beratung und Unterstützung von Kunden aus unterschiedlichen Branchen bei der Umsetzung von Projekten in den Bereichen Data Science, Data Engineering, Data Warehouse und BI:
- Telekommunikation:
Analyse der eSIM-Datenlandschaft, Entwicklung einer Datenstrategie für potenzielle Serviceverbesserungen sowie Konzeption und Implementierung von KPI- & Monitoring-Dashboards für interne und externe Stakeholder - Finanzsektor:
Überwachung, Wartung und Integration neuer ETLWorkflows in das bestehende Data Warehouse - Crowdsourcing-Plattform:
Erkennung und Analyse potenzieller Betrugskonten (z. B. Spammer, Bots) mit Methoden des maschinellen Lernens - Projektmanagement und Dokumentation
- Durchführung von Bewerbergesprächen
- Entwicklung eines Fehlerdiagnosemodells im Bereich Engine Health Monitoring
- Parsing und Übersetzung von Triebwerksdaten in maschinenlesbare Sprachen
- Verarbeitung und Datenbankarchivierung kontinuierlicher Betriebsdaten von Verkehrsflugzeugen