Data Architect

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Cummins Inc.

WebsiteLinkedInAlle Stellen

10.000+ Mitarbeiter

Gegründet 1919

⚡ Energie

🚗 Transport

🔧 Hardware

💰 €75.000.000 Grant im 2024-07

Energy • Transport • Hardware

Die Cummins Inc. ist ein weltweit führendes Unternehmen im Bereich der Antriebstechnologie, das sich mit der Entwicklung, Herstellung und dem Vertrieb einer Vielzahl von Motoren und Antriebssystemlösungen beschäftigt. Sie bieten Produkte an, die von Diesel- und Erdgasmotoren bis hin zu Hybrid- und elektrischen Antriebssystemen reichen, sowie Komponenten wie Turbolader, Kraftstoffsysteme und Emissionslösungen. Mit einem starken Fokus auf Innovation strebt Cummins danach, Emissionen zu reduzieren und die Kraftstoffeffizienz zu verbessern. Das Unternehmen ist bestrebt, Industrien bei der Umstellung auf sauberere Energie durch integrierte Antriebslösungen zu unterstützen, die für unterschiedliche Anwendungen wie Straßenverkehr, Schifffahrt, Bergbau und Bauwesen geeignet sind. Darüber hinaus bietet Cummins Dienstleistungen wie Fernüberwachung, Diagnosen und Aftermarket-Support an und unterstreicht damit sein Engagement für Nachhaltigkeit und exzellenten Kundenservice.

Beschreibung

• Design and automate scalable data ingestion and transformation pipelines across relational, event-based, and unstructured sources. • Build and maintain frameworks to monitor, detect, and resolve data quality and integrity issues. Implement data governance practices, including metadata management, data access, and retention policies. • Architect and guide development of reliable, efficient, and scalable ETL/ELT data pipelines with monitoring and alerting. • Design physical data models and optimize database structures, indexing, and relationships for performance. • Test, optimize, and troubleshoot data pipelines to ensure stability and performance. • Develop and manage large-scale data storage solutions using distributed and cloud platforms (e.g., data lakes, Hadoop, NoSQL databases). • Drive automation and modernization of data infrastructure and integration processes to support agile analytics initiatives.

🎯 Anforderungen

• College, university, or equivalent degree in relevant technical discipline, or relevant equivalent experience required. • This position may require licensing for compliance with export controls or sanctions regulations. • Intermediate experience in a relevant discipline area is required. Knowledge of the latest technologies and trends in data engineering are highly preferred and includes: • Familiarity analyzing complex business systems, industry requirements, and/or data regulations • Background in processing and managing large data sets • Design and development for a Big Data platform using open source and third-party tools • SPARK, Scala/Java, Map-Reduce, Hive, Hbase, and Kafka or equivalent college coursework • SQL query language • Clustered compute cloud-based implementation experience • Experience developing applications requiring large file movement for a Cloud-based environment and other data extraction tools and methods from a variety of sources • Experience in building analytical solutions • Intermediate experiences in the following are preferred: • Experience with IoT technology • Experience in Agile software development • Dimensional Modeling Mastery — Deep expertise in designing enterprise‑scale dimensional models (star, snowflake, constellation) with strong command of fact table grain definition, surrogate key strategies, slowly changing dimensions (Types 1–6), bridge tables, and late‑arriving data handling. • Advanced SQL Engineering — Highly proficient in writing complex, high‑performance SQL, including window functions, CTE‑driven transformations, query plan analysis, cost‑based optimization, partitioning strategies, and performance tuning across large, distributed datasets. • Snowflake Architecture & Engineering — Hands‑on experience with Snowflake internals including micro‑partitioning, clustering keys, result‑set caching layers, warehouse sizing/auto‑suspend tuning, Snowpipe/Streams/Tasks orchestration, Time Travel, Zero‑Copy Cloning, and secure data sharing patterns. • Graph Database & Cypher Proficiency — Strong experience with Neo4j or equivalent graph platforms, including graph schema design, Cypher query optimization, graph algorithms (PageRank, community detection, pathfinding), and integration of graph workloads with analytical and relational systems. • Microsoft Fabric Ecosystem — Practical experience with Fabric Lakehouse architecture, Delta Lake optimization, Data Engineering pipelines, Data Factory orchestration, KQL‑based Real‑Time Analytics, semantic model creation, and integration with Power BI and OneLake governance. • SAP S/4HANA Data Structures — Familiarity of SAP S/4HANA data models (FI/CO, MM, SD, PP), CDS views, OData services, SLT/SDI/ODP‑based extraction patterns, and harmonization of SAP transactional data into cloud‑based analytical platforms. • Cloud Data Architecture — Strong understanding of distributed data processing, ELT/ETL orchestration, event‑driven ingestion (Kafka/Event Hub), metadata‑driven frameworks, schema evolution, and data lifecycle management across cloud environments (Azure preferred). • Data Governance & Metadata Management — Experience implementing enterprise data catalogs, lineage tracking, data quality rules, master data integration, and security models (RBAC/ABAC, row‑level and column‑level security). • Performance Engineering & Optimization — Ability to diagnose bottlenecks across compute, storage, and network layers; optimize workloads for cost and performance; and design scalable, fault‑tolerant data architectures. • Cross‑Platform Integration — Experience integrating heterogeneous systems (SAP, Snowflake, Fabric, graph DBs, APIs, streaming platforms) into unified analytical ecosystems with strong focus on interoperability and data consistency.