Data Engineer
Bogotá D.C. Full-time Mid-level Data Engineering
At a glance
Mid-level Data Engineering role at Find a job at GEA. Bogotá D.C. · full-time.
Pay not stated Data Engineer salary
RoleData Engineering
SeniorityMid-level
LocationBogotá D.C.
EmploymentFull-time
PostedSep 21, 2026
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Design a shared warehouse or lake, setting its storage layers, access approach and path to scale
- Build Databricks and Azure Data Factory pipelines that bring in data from ERP, CRM and operational systems
- Set checks, cataloguing, lineage and access standards so data is reliable and traceable
- Turn business needs into data models and pipeline logic with analysts, scientists and developers
- Improve query speed, pipeline efficiency and infrastructure costs, while documenting team practices
What you bring
- At least five years in data engineering or a closely related area
- Advanced SQL skills covering complex queries, performance tuning and schema design
- Practical Databricks experience, plus a track record delivering enterprise-scale warehouses or lakes
- Professional Spanish proficiency
Who this fits
This mid-level role suits someone who can shape data architecture and build large-scale pipelines while working with technical and business partners. You’ll contribute as part of an agile team, including sprint planning and technical decisions. Working English is an advantage.
From the employer
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
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.
Did we spark your interest?
Then please click apply above to access our guided application process.
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