Design, build, and operate modern cloud-native data warehouses and robust ELT/ETL pipelines from the ground up. Collaborate with U.S. C-suite stakeholders to translate business requirements into technical architecture and ensure data security compliance.
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)
Key Responsibilities
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.
Required Skills
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.
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