• Implicarse y liderar conversaciones, modelos y decisiones que estructuran los datos de una empresa global, transformando procesos de negocio complejos en modelos de información claros, reutilizables y alineados con la estrategia de datos. • Mantener y evolucionar el Enterprise Data Model y el Data Domain Map de la organización. • Definir conceptos, taxonomías y estándares semánticos que todos puedan entender y aplicar. • Liderar reuniones con stakeholders de negocio para entender cómo funciona el negocio y modelarlo en estructuras de información consistentes. • Colaborar con equipos de datos, arquitectura de soluciones e ingeniería para asegurar la correcta implementación de los modelos en entornos basados en Google Cloud y herramientas de analítica como Power BI. • Definir e impulsar buenas prácticas de modelado en toda la organización, fomentando la adopción de modelos compartidos entre diferentes dominios de negocio. • Garantizar la alineación entre arquitectura de información, gobierno del dato y calidad de datos. • Participar en la mejora continua de herramientas como catálogos de datos y sistemas de gestión de metadatos.

• Serve as the executive-level technical leader and primary advisor for strategic customer accounts • Act as a CTO for your accounts, guiding technology decisions, roadmaps, architecture, and platform maturity • Build deep relationships with technical and business stakeholders, driving consensus on solutions • Lead Quarterly Business Reviews (QBRs), Innovation Days, roadmap presentations, and architectural vision sessions • Deliver clear recommendations, success criteria, and measurement frameworks for each engagement • Step into complex or ambiguous problem spaces and provide clarity, direction, and momentum • Collaborate closely with Account Executives to create and execute growth strategies • Identify new opportunities for phData services and lead scoping, pricing, and proposal development • Influence buying centers, build executive relationships, and create value-based narratives for expansion • Deeply understand customer needs and industry trends, positioning phData offerings accordingly • Drive strategic alignment between the customer, partners (e.g., Snowflake), and internal teams • Own delivery success and overall satisfaction across all projects within assigned accounts • Ensure delivery quality through mentorship, leadership, and hands-on involvement as needed • Provide architectural oversight, escalation management, and alignment across teams • Build technical documentation, vision statements, solution architectures, and proofs of concept • Drive accountability across internal and client teams through strategic, candid communication • Lead enablement and education of delivery teams through documentation, onboarding support, and cross-training sessions • Mentor technical consultants and engineers across the delivery organization • Build and grow technical and communication skills across teams to elevate delivery maturity • Identify and share delivery patterns that drive scalable, repeatable customer success • Actively contribute to phData internal initiatives and communities of practice

Role Description We are seeking an Engineering Subject Matter Expert to help develop next-generation AI systems by applying real-world engineering expertise to evaluate, create, and improve technical content. In this role, you will review engineering documentation, assess technical accuracy, and contribute practical engineering knowledge that enhances AI reasoning and performance. No prior AI experience is required—your engineering expertise is what matters most. Key Responsibilities - Technical Review & Analysis - Review and analyze engineering documentation, including technical reports, design documents, specifications, and project analyses. - Evaluate engineering submissions for technical accuracy, completeness, clarity, and adherence to industry best practices. - Provide detailed technical feedback, clarifications, and expert recommendations on complex engineering scenarios. - Content Development - Develop real-world engineering examples, case studies, and problem sets across various engineering disciplines. - Create structured technical explanations to help improve AI understanding of practical engineering concepts. - Ensure all technical content is accurate, well-organized, and aligned with engineering principles. - AI Model Evaluation - Assess AI-generated engineering responses for correctness, logical reasoning, and technical rigor. - Identify errors, inconsistencies, or gaps in engineering outputs and provide actionable feedback to improve model performance. - Contribute to the development of high-quality engineering datasets for AI training and evaluation. - Collaboration & Process Improvement - Work closely with project teams to ensure deliverables align with project milestones and quality standards. - Share industry best practices and technical insights to enhance project methodologies. - Collaborate effectively in a remote environment while maintaining high-quality documentation and deliverables. Qualifications - Bachelor's, Master's, or PhD in Mechanical, Civil, Electrical, Chemical, Industrial, Aerospace, Electronics, Manufacturing, or any other Engineering discipline. - Experience preparing technical reports, engineering documentation, design specifications, or project analyses. - Strong analytical and problem-solving skills with attention to technical detail. - Excellent written and verbal communication skills with the ability to explain complex engineering concepts clearly. - Ability to work independently and manage deliverables in a remote work environment. Preferred Qualifications - Experience working on multidisciplinary or cross-functional engineering projects. - Ability to communicate technical concepts effectively to both technical and non-technical audiences. - Familiarity with engineering standards, regulatory compliance, quality assurance, or industry best practices. - Experience reviewing engineering documentation or providing technical guidance. - Passion for innovation, technology, and engineering education. Must-Have Skills - Engineering Documentation - Technical Analysis - Technical Report Writing - Problem Solving - Engineering Design Review Good-to-Have Skills - Quality Assurance - Regulatory Compliance - Technical Documentation - Cross-functional Collaboration - Engineering Standards - Project Analysis - Design Documentation - Research & Technical Writing

• Design the ETL, transformation, and modeling patterns the team builds on • Build and maintain data ingestion pipelines that move data reliably from source into the warehouse • Build and maintain transformation models — client-specific and shared • Own data quality monitoring end-to-end: define what we monitor and to what SLA — not just tune thresholds — and decide where to spend the coverage budget • Understand the full data flow from raw event ingestion through final reporting tables • Own the complex, ambiguous requests and build the self-serve tooling that keeps the routine queue off engineering's plate