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Data Engineer - Core Systems Germany Jobs

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Senior Data Platform Engineer
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Germany , Berlin
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Zalando
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IV&V Engineer
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Germany , Darmstadt
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Sopra Steria
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Senior Data Platform Engineer
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Germany , Berlin
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Zalando Sverige
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Avionics Systems Engineer - Communication
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Germany , Munich
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Helsing
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Engineering Manager
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Germany , Berlin
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Cherry Ventures
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Principal Engineer
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Germany , Berlin
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Intercom
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System Engineer Multi-Tenant Data Centres EMEA
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Germany , Berlin
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Beautiful Recruitment
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Senior Data Engineer
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Germany , Berlin
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Aiven Deutschland GmbH
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Principal Engineer
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Germany , Berlin
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Aignostics
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Senior Machine Learning Engineer
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Germany , Berlin
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Aignostics
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Unsolicited application
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Germany , Berlin
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Project A Ventures GmbH
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Software Developer / Data Engineer in Proteomics
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Germany , Freising
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jobRxiv
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Senior Data Engineer
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Germany , Berlin
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ib vogt GmbH
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Senior Data Engineer
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Germany , Berlin
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Audibene GmbH
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Spacecraft Operations Engineer
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Germany , Darmstadt
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Sopra Steria
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Satellite Image Navigation, Registration and Calibration Engineer
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Germany , Darmstadt
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Data Processing System Engineer
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Germany , Darmstadt
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PhD Student Chassis Control Systems
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Germany , Wolfsburg
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Volkswagen AG
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PhD Student Generative AI Autonomous Process Planning Systems
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Volkswagen AG
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Internship AI Methods Vehicle Safety
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Germany , Wolfsburg
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Volkswagen AG
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Explore Data Engineer - Core Systems jobs and discover a career at the intersection of data infrastructure and fundamental business operations. Data Engineers specializing in Core Systems are the architects and builders of the reliable, scalable data pipelines that serve as the central nervous system for an organization. Unlike roles focused solely on analytics or machine learning, these professionals ensure that mission-critical internal systems—such as ERP, CRM, supply chain, and financial platforms—have robust, integrated, and trustworthy data flows. Their work directly underpins daily business functions, reporting, and operational intelligence. In this pivotal role, typical responsibilities revolve around designing, constructing, and maintaining the data architecture that supports core enterprise applications. This involves developing and managing ETL (Extract, Transform, Load) or ELT processes to consolidate data from various transactional systems into centralized data warehouses or data lakes. A core systems data engineer ensures data quality, consistency, and availability for downstream consumption, enabling other teams to perform accurate analysis and reporting. They are also responsible for optimizing data storage and retrieval for performance, implementing data governance and security protocols, and building monitoring systems to guarantee pipeline health and data integrity. Collaboration is key, as they frequently work with software engineers, database administrators, and business analysts to understand data requirements and translate them into technical solutions. The typical skill set for these jobs is a blend of strong software engineering principles and deep data expertise. Proficiency in SQL is fundamental, alongside advanced programming skills in languages like Python, Java, or Scala. Experience with cloud data platforms (AWS, Azure, GCP) and their associated services (e.g., Redshift, BigQuery, Azure Data Factory, S3) is highly common. Knowledge of data pipeline orchestration tools like Apache Airflow, data warehousing concepts, and data modeling techniques (dimensional modeling, data vault) is essential. Furthermore, familiarity with version control (Git), infrastructure-as-code, and an understanding of the specific data domains within core business systems (finance, logistics, etc.) are significant assets. Soft skills include problem-solving, clear communication to bridge technical and business teams, and a meticulous attention to detail to safeguard critical operational data. For those seeking Data Engineer - Core Systems jobs, this career path offers the challenge of building foundational infrastructure with a direct, tangible impact on business efficiency and decision-making. It is ideal for systematic thinkers who enjoy creating order from complexity and ensuring that an organization's most vital data assets are managed with precision and reliability.

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