Data Engineer

Responsibilities / 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.

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 teams 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.

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