AI Native Data Architect

PALO IT; Palo Alto; Presencial; MXN 2.615.974 - 4.011.160; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

International mobility

Career growth

Learning & certifications support

Collaborative culture

Entrepreneurship & intrapreneurship

Descripción de la vacante

PALO IT is seeking a Data Architect to lead design and evolution of scalable data architectures across cloud platforms. You will collaborate with data engineers, analytics teams, business stakeholders, and IT leadership to define data strategies that drive decision making and digital transformation.

You will model enterprise data, govern data quality and privacy, implement Data Mesh and Lakehouse patterns, and guide teams through data integration, tooling, and governance.

Formación

• 10+ years of experience in data engineering.
• 3+ years designing cloud-based data architectures (Azure, AWS, or GCP).
• 2+ years in data architecture, enterprise data modeling, or data governance.
• Led data model design (relational, multidimensional, non-relational) for Data Warehouse, Data Lake, or Lakehouse architectures.
• Participated in multi-source data integration projects (on-premise, cloud, external sources).
• In-depth knowledge of data governance frameworks including quality, cataloging, privacy, and compliance.
• Experience with modern architectures (Data Mesh, Lakehouse) and cataloging tools (Purview, Unity Catalog) is a plus.

Responsabilidades

• Define, evolve, and document the organization’s data architecture aligned with business and IT strategy.
• Design enterprise data models (conceptual, logical, and physical), establishing naming conventions and modeling standards.
• Assess and recommend data technologies based on evolving business needs (Data Lake, Lakehouse, Mesh, Warehouse).
• Define policies and standards for data governance, quality, privacy, cataloging, and lineage.
• Lead adoption of metadata management and data discovery tools across teams.
• Ensure compliance with data regulations and security requirements.
• Architect data integration solutions (ETL/ELT, real-time and batch pipelines).
• Ensure interoperability across domains, sources, and consumers using Data Mesh principles.
• Define integration patterns and data federation frameworks to deliver a 360° data view.
• Act as a technical reference in data architecture for engineering and analytics teams.
• Promote adoption of data models, standards, and best practices across the organization.
• Translate business needs into scalable data solutions and facilitate technical-business alignment.

Conocimientos

Systemic thinking

Cross-functional communication

Technical leadership

Complexity management

Product & platform mindset

Curiosity & continuous learning

Educación

Bachelor’s degree in Mechatronics Engineering, Applied Mathematics, Software Engineering, Computer Science, or related fields

Herramientas

Azure Synapse

Azure Data Lake Gen2

Purview

Unity Catalog

Cosmos DB

Azure Data Factory

Azure Databricks

Terraform

Azure DevOps

SQL

Python

Java

Scala

Descripción del empleo

Your Role

As a Data Architect, you will take a leading role in designing, evolving, and optimizing data architecture for innovative, scalable, and secure solutions. You will collaborate closely with data engineers, analytics teams, business stakeholders, and IT leadership to deliver data strategies that power decision‑making and digital transformation.

Key Responsibilities

• Define, evolve, and document the organization’s data architecture aligned with business and IT strategy.
• Design enterprise data models (conceptual, logical, and physical), establishing naming conventions and modeling standards.
• Assess and recommend data technologies (Data Lake, Lakehouse, Mesh, Warehouse) based on evolving business needs.
• Define policies and standards for data governance, quality, privacy, cataloging, and lineage.
• Lead adoption of metadata management and data discovery tools across teams.
• Ensure compliance with internal and external data regulations and security requirements.
• Architect data integration solutions (ETL/ELT, real‑time and batch pipelines).
• Ensure interoperability across domains, sources, and consumers using principles such as Data Mesh.
• Define integration patterns and data federation frameworks to deliver a 360° data view.
• Act as a technical reference in data architecture, guiding engineering, analytics, and business teams.
• Promote adoption of data models, standards, and best practices across the organization.
• Translate business needs into scalable data solutions and facilitate technical‑business alignment.

Who You Are

Education

• Bachelor’s degree in Mechatronics Engineering, Applied Mathematics, Software Engineering, Computer Science, or related fields.

Required Experience

• 10+ years of experience in Data engineering.
• 3+ years designing cloud‑based data architectures (Azure, AWS, or GCP).
• 2+ years in data architecture, enterprise data modeling, or data governance.
• Led data model design (relational, multidimensional, non‑relational) for Data Warehouse, Data Lake, or Lakehouse architectures.
• Participated in multi‑source data integration projects (on‑premise, cloud, external sources).
• In‑depth knowledge of data governance frameworks including quality, cataloging, privacy, and compliance.
• Experience with modern architectures (Data Mesh, Lakehouse) and cataloging tools (Purview, Unity Catalog) is a plus.

Technical Expertise

• Data modeling: conceptual, logical, physical modeling; normalization; relational and non‑relational design.
• Architectures: Data Warehouse, Data Lake, Lakehouse, Data Mesh.
• Governance: Data lineage, quality, privacy, RBAC, metadata management.
• Platforms: Azure Synapse, Azure Data Lake Gen2, Purview, Unity Catalog, Cosmos DB.
• Data integration: Azure Data Factory, API Management, integration patterns, Azure Databricks.
• Infrastructure as Code (IaC): Terraform, Azure DevOps (preferred).
• Languages & tools: SQL, Python (architectural level), JSON, Java, Scala.
• CI/CD: Git, Sonar, DevOps best practices.

Leadership & Soft Skills

• Systemic thinking: designs modular, scalable, and integrated architectures.
• Cross‑functional communication: translates technical and business requirements clearly and effectively.
• Reuse mindset: focuses on creating shareable and scalable components.
• Technical leadership: influences technical direction and decision‑making across teams.
• Complexity management: solves high‑impact, large‑scale technical challenges.
• Product & platform mindset: designs with the data consumer experience in mind.
• Curiosity & continuous learning: stays ahead of tech trends and promotes innovation.

AI‑Native Engineering (Core Expectation)

• Use generative AI coding tools (e.g., GitHub Copilot, Cursor) for code scaffolding, refactoring, generation, and optimisation.
• Generate test‑cases and documentation.
• Build applications through AI‑driven development practices.
• AI‑assisted debugging and troubleshooting.
• Intelligent code completion and pattern recognition.
• Automated documentation generation.
• Apply prompt engineering best practices for reliable, repeatable engineering outcomes.
• Validate GenAI output (determinism checks, guardrails, fallback logic).

What We Offer

• Stimulating, collaborative work environments.
• A personalized career path and growth opportunities.
• International mobility and cultural exchange.
• Internal R&D and innovation programs.
• Learning & knowledge‑sharing initiatives.
• Training and certification support.
• Entrepreneurship & intrapreneurship culture.