Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com We are seeking a Data Engineer Analyst to contribute to our next level of growth and expansion. What is this position about? We are looking for a Data Engineer Analyst to contribute to the design and implementation of scalable, domain-oriented data solutions for a high-impact enterprise initiative. You will work within a specific data domain — such as Ingestion, Customer Data, Enrichment, or Marketing Signals — ensuring high-quality, reliable data pipelines that power analytics and business-critical workflows. You may work 100% remotely from anywhere in Mexico! Contribute to the design and development of scalable data pipelines on AWS (Glue, S3, Athena, Step Functions). Build and optimize batch and streaming ingestion pipelines, including CDC-based architectures. Support the design and implementation of data models aligned with business and analytics needs. Ensure data quality, reliability, and performance across pipelines and datasets. Collaborate with cross-functional teams to align technical solutions with business requirements. Apply best practices in data engineering, including modular design, reusability, and governance. Leverage AI-assisted development tools to improve productivity and code quality. Proactively identify risks, bottlenecks, and optimization opportunities across data workflows. Technical Requirements Ability to design and implement AI/automation Proof of Concepts (POCs) for order intake workflows. Experience validating completeness and accuracy of outputs for processed order packages. Capability to generate and validate customer acknowledgment outputs based on processed data. Experience creating or simulating document management workflows, including SharePoint folder structures, document routing, and filing logs. Ability to generate structured outputs compatible with downstream systems (e.g., Odoo-ready payloads, CSV, spreadsheets, JSON, or sandbox job outputs). Strong experience producing validation reports, including accuracy metrics, exception handling, estimated efficiency gains, and failure mode analysis. Ability to assess solution performance and provide go/no-go recommendations, including scalability and production readiness considerations. What about languages? English: Advanced (required for effective communication with global teams). How much experience must I have? 3+ years of experience in Data Engineering or related disciplines, with hands-on work building and maintaining data pipelines on cloud platforms such as AWS. Our Perks and Benefits: 🏥 Health and Well-being: At-home medical assistance via EMI (or similar provider) through Asobursatil, available for all employees from AllStar to Analyst level. Private healthcare plans for Lead-level roles and above. 🎉 Celebrations and Recognitions: Christmas kit delivered to all employees. 1 day off for academic graduation. Family Day: 1 day off every semester (must be taken within the same semester). 💰 Financial Health and Savings (Work Together, Get Together Program): Savings incentive program via Asobursatil: Year 1: Blend contributes 50% of your monthly savings. Year 2: Blend contributes 100% of your monthly savings. Year 3+: Blend contributes 150% of your monthly savings. Savings can be withdrawn in July and December. 📚 Educational Loans and Subsidies: Forgivable education loans subject to committee approval and budget availability. Requirements: 1+ year at Blend, no disciplinary actions in the past 6 months, successful completion of prior training, and knowledge sharing within 6 months post-training. Retention-based forgiveness schedule applies after program completion. So what are the next steps? Our team is eager to learn about you! Send us your resume or LinkedIn profile below and we'll explore working together!