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.

📌 Data Engineer (Bogotá)
🏢 Gea
📍 Bogotá