Data Engineer

Description

The Data Engineer is responsible for designing, developing, and maintaining scalable data pipelines, databases, and analytics platforms that support cloud-based digital applications for food and beverage processing plants. Working closely with Product Owners, Software Developers, Automation Engineers, and Process Experts, this role enables advanced analytics, AI solutions, predictive maintenance, asset performance monitoring, and operational reporting. This position can be based in Alcobendas (Spain), Naas (Ireland) or Bogota (Colombia), depending on the selected candidate.

About GEA

• GEA is one of the largest suppliers for the food and beverage processing industry and a wide range of other process industries. Approximately 18,000 employees in more than 60 countries contribute significantly to GEA’s success – come and join them! We offer interesting and challenging tasks, a positive working environment in international teams and opportunities for personal development and growth in a global company.

• Why join GEA

• GEA is an equal opportunity employer. Applicants will therefore receive consideration for employment without regard to age, sex, race, color, religion, world view, national origin, genetics, disability, gender identity, marital status, sexual orientation, veteran status or any other protected characteristic required by applicable law. Applicants with disabilities are welcome and will be given special consideration if they are equally qualified.

Your profile and qualifications

• Bachelor's or Master's Degree in Computer Science, Data Science, Engineering, or a related field.

• Microsoft Azure or Databricks Certifications.

• 3-7 years of experience in Data Engineering, Cloud Data Platforms, or Analytics Solutions.

• Experience developing Azure cloud-based data solutions.

• Experience working with large volumes of time-series and industrial data.

• Strong knowledge of:Data ModellingData GovernanceMetadata ManagementTime-Series Database DesignQuery OptimizationWorkflow OrchestrationREST APIs

• Data Modelling

• Data Governance

• Metadata Management

• Time-Series Database Design

• Query Optimization

• Workflow Orchestration

• REST APIs

• Proven experience working in Agile/Scrum environments.

• Awareness of IEC 62443 standards.

• Desirable knowledge of Machine Learning, AI, and Data Science concepts, with experience supporting AI-driven analytics solutions.

• Fluent English (B2/C1) required.

• Spanish or German would be considered a strong advantage.

• Strong ethics, compliance, and professionalism

• Commitment and alignment with business strategies and policies

• Customer-focused and sustainability-oriented mindset

• Cross-functional and multidisciplinary collaboration

• Excellent communication and networking skills

• Strong listening skills and cultural awareness

• Strong analytical, problem-solving, and decision-making skills

• Continuous improvement mindset and commitment to innovation

• Adaptability and willingness to learn new tools, technologies, and methodologies

• Attractive compensation package aligned with experience & responsibilities

• Private health insurance plan

• Employee Assistance Program

• Flexible working hours and a hybrid working model

• 23 days of vacation per year

• Great work environment as part of a collaborative team

• Continuous internal training and career development opportunities, both nationally and internationally

• The opportunity to join a company recognized as a Top Employer 2026

Your responsibilities and tasks

• Design, develop, and maintain data solutions leveraging Microsoft Azure, Databricks, InfluxDB, Industrial IoT data sources, and modern data engineering practices.

• Design, develop, and maintain ETL/ELT pipelines to support data integration and analytics.

• Design data models, relationships, and schemas that support analytics, reporting, machine learning, and digital products.

• Develop API-based integrations using REST services and JSON.

• Establish standards for data quality, governance, security, and lifecycle management.

• Ensure data architectures are scalable, reliable, and aligned with enterprise standards.

• Support the integration of operational and equipment data from industrial systems into enterprise and cloud platforms.

• Collaborate with cross-functional teams to deliver data-driven solutions and enable business value through analytics and AI.