Data Architect

About the role

Build. Scale. Sustain. PALO IT is a global technology consultancy dedicated to building digital and sustainable products and services that unlock value across the triple bottom line: people, planet, and profit. We do the right thing, and we do it right. We're proud to be a B Corp-certified company and a World Economic Forum New Champion . We are small enough to care locally, big enough to deliver globally (5 continents, 18 offices, 650+ experts, 50+ nationalities) We are resilient and independent (0 debt, 100% privately owned) We are entrepreneurs and passionate experts who invest in what we believe in and work as a collective intelligence We are positive, courageous, caring, committed, and excellence-driven About Gen-e2 While the market is still focused on AI-augmenting delivery, we've redefined the SDLC to be AI First . With our Gen-e2 framework: We now generate 95% of the product (code, documentation, infrastructure as code, and design) using GitHub Copilot. Output quality consistently exceeds that of our best traditional engineering teams. A centralized product repository gives AI full context of all product artifacts. Enterprise-grade standards and rules are embedded in prompts and code libraries. We deliver full products 2-3x faster , raising the bar for engineering excellence. 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.

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.

Requirements

• 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

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