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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Rheinmetall - **Location:** UK - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Apache HTTP Server, Big Data, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Linux, Distributed Computing Environment, Distributed Data Store, Graph Database, Information Lifecycle Management, Python (Programming Language), Machine Learning, Metadata, Open Source Technology, Software Architecture, Software Engineering, SQL Databases, Data Streaming, Management of Software Versions, Apache Spark, Indexer, Data Lakes, Semi-structured Data, Kubernetes, Information Technology, Data Lineage, Apache Flink, Apache Kafka, Data Management, Machine Learning Operations, Data Objects, Entity Resolution, Data Pipelines, Devsecops, Programming Languages - **Published:** October 17, 2026 - **Apply:** https://www.totaljobs.com/job/data-engineer/rheinmetall-job108195082 ## About the Role * A degree in computer science, data engineering, software engineering, business informatics, or a comparable practical qualification * Several years of hands-on experience in data engineering and in building or further developing distributed data platforms * A very good understanding of modern data lake/lakehouse architectures, object storage, and the fundamental trade-offs between storage, compute, data movement, performance, and costs * Demonstrable hands-on experience with data pipelines, data modeling, SQL, and at least one relevant programming language, preferably Python * Solid knowledge of metadata, data quality, schema management, data lineage, and reproducible processing * Practical understanding of Linux, containers, and Kubernetes, as well as the ability to independently implement, operate, and analyze data components in a distributed platform * Ability to link architectural decisions to practical implementation and to systematically investigate problems down to their technical root causes * A structured, team-oriented approach to work, as well as strong written and spoken English skills * Experience with very large volumes of image, video, sensor, or geodata * Knowledge of ontologies, knowledge graphs, semantic data models, entity resolution, or comparable approaches * Experience with technologies for distributed data processing and streaming, e.g., Spark, Flink, Trino, Kafka, or comparable solutions * Experience with open Lakehouse table formats or comparable technologies, such as Apache Iceberg, Delta Lake, or Apache Hudi * Experience in provisioning and versioning data for machine learning, MLOps, AI, or autonomy workloads * Knowledge of Infrastructure as Code, GitOps, or automated platform and deployment processes * Experience with highly available, on-premises, edge, or intermittently disconnected data platforms A solid foundation and practical implementation experience are crucial for this role. Additional specialized knowledge is expressly not a requirement. ## Description * Design, further development, and practical implementation of a scalable data lakehouse and data platform architecture for very large, heterogeneous data sets * Developing a long-term data and storage strategy that takes into account performance, scalability, data lifecycle, costs, availability, and operational requirements * Designing suitable storage and access concepts for large volumes of image, video, sensor, geospatial, and other unstructured and semi-structured data, including metadata, partitioning, indexing, compression, and archiving * Building and operating robust batch and streaming pipelines for the ingestion, processing, transformation, and delivery of data from heterogeneous sources * Co-design and implementation of a semantic data model or ontology that enables data objects, entities, relationships, and context to be described and linked across systems * Creating a resilient data foundation for machine learning, AI-based applications, and autonomous systems, including traceable data provenance and reproducible training and validation datasets * Ensuring data lineage, data versioning, data quality, schema management, and appropriate governance and access concepts throughout the entire data lifecycle * Development and operation of the data platform in large, distributed Kubernetes-based infrastructures, as well as integration into Infrastructure-as-Code and CI/CD/DevSecOps processes * Analyzing and optimizing data layouts, data locality, compute proximity, and data movements for data-intensive applications * Evaluation of suitable open-source and commercial technologies, as well as implementation ranging from proofs of concept to production-ready, scalable solutions * Close collaboration with AI/ML, software, platform, and cybersecurity teams on architectural decisions, interfaces, data products, and operational implementation