Data Engineer

Gea Group; Bogotá ciudad; Presencial; COP 135 936 000 - 233 032 000; Tempo integral

Resumo da oferta

GEA is seeking a Data Engineer to design and implement a unified data warehouse/data lake architecture supporting analytics and AI workloads. You will build ETL/ELT pipelines, ensure data quality and governance, and collaborate with data scientists, BI analysts and developers in an agile environment.

The role requires strong SQL, Databricks experience, and proficiency with data modeling and enterprise-scale solutions; Spanish is required, English is a plus.

Qualificações

• 5+ years of professional experience in data engineering.
• Expert SQL with performance tuning and modeling.
• Databricks experience for large-scale data processing.
• Experience with data warehouses or data lakes at enterprise scale.
• ETL/ELT patterns and data modeling (star, data vault).
• Spanish professional proficiency; working English is a strong advantage.

Responsabilidades

• Design and implement unified data warehouse/data lake for multiple analytics workloads.
• Build and maintain ETL/ELT pipelines from ERP, CRM, and other sources using Databricks and Azure Data Factory.
• Establish data quality standards, governance, cataloguing and lineage.
• Collaborate with data scientists, BI analysts and developers to meet downstream needs and translate requirements.
• Monitor query performance, pipeline efficiency and cost; stay updated on tooling.

Conhecimentos

SQL

Databricks

Azure Data Factory

Data Modeling

Ferramentas

Databricks

Azure Data Factory

Descrição da oferta de emprego

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

Responsibilities / Tasks

Data Architecture & Infrastructure

• 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.

Pipeline Development & Integration

• 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.

Data Quality & Governance

• 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.

Collaboration & Stakeholder Engagement

• 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.

Continuous Improvement

• 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.

Your Profile / 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.