Data Engineer

Description

GEA is one of the world’s largest systems suppliers for the food, beverage and pharmaceutical sectors. Our portfolio includes machinery and plants as well as advanced process technology, components and comprehensive services. Used across diverse industries, they enhance the sustainability and efficiency of production processes globally. We are looking for a Data Engineer

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

• Minimum 5 years of professional experience in data engineering or a closely related field.

• Expert-level proficiency in SQL — including complex query design, performance tuning, and schema modeling.

• Hands-on experience with Databricks for large-scale data processing and pipeline orchestration.

• Proven experience designing and implementing data warehouse or data lake solutions at enterprise scale.

• Strong understanding of ETL/ELT design patterns, data modeling methodologies (star schema, data vault, etc.), and pipeline orchestration.

• Experience integrating data from heterogeneous source systems (ERP platforms, APIs, flat files, operational databases).

• Ability to communicate technical concepts clearly to both technical and non-technical audiences.

• Professional-level proficiency in Spanish; working English is a strong advantage.

Your responsibilities and tasks

• Design and implement a unified data warehouse and/or data lake capable of serving multiple analytics and AI workloads.

• Define the overall data architecture strategy, including storage layers, access patterns, and scalability approach.

• Define the data extraction and landing strategy, partitioning and SCD (slowly changing dimensions), data modelling strategy and consumption ports.

• Build and maintain ETL/ELT pipelines consuming data from multiple enterprise source systems (ERP, CRM, operational tools, and others).

• Develop pipelines using Databricks (Lakeflow Connect) & Azure Data Factory as primary platforms.

• Ensure pipeline reliability, scalability, and observability through monitoring, alerting, and logging.

• Establish and enforce data quality standards, validation rules, and anomaly detection processes.

• Implement data cataloguing, lineage tracking, and documentation practices to ensure transparency and auditability.

• Define naming conventions, schema standards, and access control policies in coordination with stakeholders.

• Work closely with data scientists, BI analysts, and developers within the team to ensure data products meet downstream requirements.

• Translate business requirements from non-technical stakeholders into robust data models and pipeline logic.

• Actively contribute to sprint planning and technical decision-making within an agile team environment.

• Monitor and optimize query performance, pipeline efficiency, and infrastructure cost.

• Stay current with developments in data engineering tooling, cloud platforms, and best practices.

• Contribute to the team's knowledge base through documentation and internal knowledge-sharing.