[Hiring] Data Engineer @Fresh Consulting

Role Description

Fresh Consulting is seeking a Senior Data Engineer (Individual Contributor) to technically lead the design and implementation of cloud-native data platforms for international clients in the US. The successful candidate will not only maintain existing data flows but also design, build, and implement a modern data warehouse from the ground up, laying the foundation for future AI/LLM integrations and automated workflows.

Location: Remote (Mexico — Preferred: Monterrey, Mexico City, Guadalajara)

Language Requirement: Fluent professional English (Minimum CEFR C1)

Travel: Availability to travel to the U.S. quarterly (approximately 1 week per trip)

Qualifications

• Microsoft Fabric (highly preferred)
• OR portable stacks (BigQuery, Snowflake, Databricks)
• 4 to 7 years of verifiable experience building end-to-end cloud data warehouses (real projects taken from zero to production), not just maintaining existing systems
• Cloud Infrastructure: Azure data stack (Data Factory, Synapse, Fabric) or equivalent on AWS/GCP
• Core Languages: Advanced SQL (dimensional modeling, performance optimization) and Python (transformation, orchestration, scripting)
• BI Tools: Power BI or equivalent (Looker, Tableau, Mode)
• Orchestration and Transformation: Azure Data Factory, Airflow, dbt, Dagster, or similar tools
• Direct Exposure to Stakeholders: Experience working directly with executives in the U.S.
• Valid US Visa (Minimum B1/B2): Confirmed availability to travel to the US quarterly

Requirements

• Design and Construction: Design, build, and operate modern, end-to-end, cloud-native data warehouses
• Pipeline Development: Develop and maintain robust ELT/ETL pipelines that integrate multiple internal systems (servicing, origination, CRM) and third-party data feeds
• Data Modeling: Create scalable data models for reporting, BI (semantic layer), and future AI/ML initiatives
• Governance and Security: Implement data quality, governance, and security practices aligned with stringent U.S. financial regulations
• Executive Collaboration: Work closely with C-suite stakeholders and directors in the U.S. to translate business requirements into technical data architecture
• Optimization: Optimize the performance of queries, pipelines, and workflows to ensure efficiency and cost control