Data Engineer

As a Data Engineer, you will be responsible for designing, developing, and maintaining scalable data solutions that support reporting, analytics, and business decision-making. You will work with large datasets and modern cloud technologies to build data pipelines, transform and integrate data, and deliver trusted datasets for business intelligence solutions.

The role requires hands-on experience with SQL, Python, data modeling, ETL/ELT development, Azure Synapse Analytics, Microsoft Fabric, Data Factory Pipelines, and Notebooks. You will collaborate with Solution Architects, Business Intelligence teams, and business stakeholders to deliver reliable and efficient data platforms and analytical solutions.

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

• Design, develop, and maintain enterprise-grade data pipelines using Microsoft Fabric and Azure Synapse Analytics.
• Build, optimize, and support ETL/ELT processes for ingesting and transforming data from multiple source systems.
• Develop and maintain Azure Synapse Pipelines, Fabric Data Factory Pipelines, and orchestration workflows.
• Create, maintain, and optimize Notebooks using Python, PySpark, and SQL for data transformation and validation activities.
• Design and implement data models that support reporting, self-service analytics, and dashboard requirements.
• Develop complex SQL queries, views, stored procedures, and transformation logic.
• Build reusable data engineering solutions for automation, monitoring, and operational reporting.
• Monitor pipeline performance, troubleshoot failures, and implement proactive alerting and monitoring mechanisms.
• Perform data quality assessments, data reconciliation, and root-cause analysis for production issues.
• Work with Lakehouse, Data Warehouse, and cloud-based analytical platforms to support enterprise reporting initiatives.
• Collaborate with Business Intelligence and Solution Design teams to deliver reporting-ready curated datasets.
• Participate in solution architecture discussions and recommend scalable and efficient data engineering approaches.
• Maintain technical documentation, data lineage, process flows, and operational runbooks.
• Support deployment activities, release management, and CI/CD best practices.
• Mentor junior team members and contribute to continuous improvement initiatives.

• Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or related discipline.
• 2–4 years of experience in Data Engineering, Analytics Engineering, or Business Intelligence.
• Strong experience with SQL and relational database concepts.
• Hands-on experience with Python for data transformation and automation.
• Experience building ETL/ELT solutions using Azure Synapse Analytics, Microsoft Fabric, Azure Data Factory, or similar platforms.
• Experience developing and supporting Data Pipelines and Notebooks.
• Knowledge of Data Warehousing and Dimensional Data Modeling concepts.
• Experience working with cloud-based analytics platforms and modern data architectures.
• Strong analytical, troubleshooting, and problem-solving skills.
• Good verbal and written communication skills.