Senior Manager Data Engineering

Expect more. Connect more. Be more at Diebold Nixdorf. Our teams automate, digitize, and transform the way more than 75 million people around the globe bank and shop in this hyper-connected, consumer-centric world. Join us in connecting people to commerce in this vital, rewarding role.

Provides leadership across Data Engineering, data platform architecture, and enterprise data modernization. Designs, develops, and evolves scalable approaches for how data is ingested, stored, transformed, governed, integrated, and consumed across the organization.

Partners with Data Engineering, Analytics, AI, Architecture, Security, and business teams to establish modern engineering standards and data architectures. Brings strong experience with cloud data platforms, including Microsoft Fabric, medallion architecture, semantic layers, ontology, metadata, knowledge graphs, and data governance.

Why should you join Diebold Nixdorf?

Brightest minds + technology and innovation + business transformation The people of Diebold Nixdorf are 23,000+ teammates of diverse talents and expertise in more than 130 countries, harnessing future technologies to deliver personalized, secure consumer experiences that connect people to commerce. Our culture is fueled by our values of collaboration, decisiveness, urgency, willingness to change, and accountability.

-Diebold Nixdorf is an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, gender identity, age, marital status, veteran status, or disability status.

To all recruitment agencies: Diebold Nixdorf does not accept agency resumes. Please do not forward resumes to our jobs alias, Diebold Nixdorf employees or any other organization location. Diebold Nixdorf is not responsible for any fees related to unsolicited resumes

We are a global Company operating in multiple Locations and Entities. As we are keen to find the best solution for our candidates several legal entities might be applicable for a Job offer. A List of our operating entities can be found here - https://www.dieboldnixdorf.com/en-us/about-us/global-locations

• Bachelor''s Degree or equivalent work experience required.

• 8 to 10 years of experience in Data Engineering, Data Architecture, or Data Platforms, with 2-4 years of leadership experience.

• Strong knowledge of cloud data platforms, lakehouse and medallion architecture, ETL/ELT, data modeling, and data governance.

• Experience with Microsoft Fabric, Azure data services, OneLake, or comparable modern data platforms.

• Understanding of semantic layers, metadata management, ontology, and knowledge graph concepts.

• Fluent written and verbal English communication skills.

• Provide technical leadership across Data Engineering and data platform architecture.

• Work with business and technology stakeholders to understand objectives and define scalable data architectures.

• Define standards for data ingestion, ETL/ELT, transformation, storage, integration, and consumption.

• Establish modern lakehouse and medallion architecture patterns for enterprise data platforms.

• Partner with Data Engineering teams to improve engineering practices, platform capabilities, reliability, and performance.

• Establish and advance data governance practices across data quality, lineage, metadata, access, retention, and lifecycle management.

• Develop semantic layer, ontology, and knowledge graph capabilities to improve consistency and usability of enterprise data.

• Identify opportunities to apply new technologies and processes that improve scalability, efficiency, and business value.

• Provide technical leadership across Data Engineering and data platform architecture.

• Work with business and technology stakeholders to understand objectives and define scalable data architectures.

• Define standards for data ingestion, ETL/ELT, transformation, storage, integration, and consumption.

• Establish modern lakehouse and medallion architecture patterns for enterprise data platforms.

• Partner with Data Engineering teams to improve engineering practices, platform capabilities, reliability, and performance.

• Establish and advance data governance practices across data quality, lineage, metadata, access, retention, and lifecycle management.

• Develop semantic layer, ontology, and knowledge graph capabilities to improve consistency and usability of enterprise data.

• Identify opportunities to apply new technologies and processes that improve scalability, efficiency, and business value.