AI Native Data Architect

Who We Are

Building the AI-first frontier enterprise.

We are a global technology consultancy with a trademarked, AI-first approach—Gen-e2™. It redefines how enterprises build digital products and transform their organizations with AI. We do the right thing, and we do it right. We're proud to be a World Economic Forum New Champion, and a B Corp-certified company.

• We are small enough to care locally, big enough to deliver globally (10 countries, 450+ experts from 50+ nationalities)
• We are becoming an agentic organization, adopting the AI-native operating model we bring to our clients.
• We are robust and resilient (100% independent, 0 debt, founded 2009)
• We are AI-native professionals who invest in what we believe and work as a collective intelligence
• We are positive, courageous and deliver at the leading edge.

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

Strategy & Architecture

• 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.

Governance, Privacy & Quality

• 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.

Data Integration & Design

• 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.

Collaboration & Leadership

• 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.
• Preferred certifications: Microsoft Certified, Azure Data Engineer Associate, or relevant cloud and data architecture certifications.

Required Experience

• 10+ years of experience in Data engineer
• 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) as a first-class engineering assistant for:
• Code scaffolding and refactoring
• Code generation and optimisation
• Test-cases and documentation generation
• Build applications through AI-driven development practices, including:
• 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).

More About PALO IT

Our clients include some of the world’s most successful companies. We collaborate with leading enterprises, next-generation businesses and frontier partners, shaping what comes next, helping them scale AI and solve complex business and technology challenges.

What We Offer

• Stimulating working environments
• Unique career path
• International mobility
• Internal R&D projects (including Gen-e2™)
• Knowledge sharing
• Personalized training via PALO IT Academy
• Entrepreneurship & intrapreneurship

For more on our team culture and benefits, check out our careers page.