Data Architect Jobs

About The Position

Requirements

• Mastery of enterprise data architecture frameworks (TOGAF, DAMA-DMBOK, Zachman) with the ability to design and govern conceptual, logical, and physical data models across structured, semi-structured, and unstructured sources in a global, multi-business-unit environment.
• Deep expertise in cloud data platform architecture spanning data warehouses, data lakes, and lakehouse patterns including the ability to design scalable, cost-optimized infrastructure and embed DataOps, CI/CD, and real-time streaming capabilities into enterprise data operations.
• Proficiency in Master Data Management methodologies, data quality frameworks, and end-to-end data lineage tracking, ensuring that enterprise data assets are accurate, traceable, and trusted from source systems through to analytics consumption.
• Ability to design governed, self-service analytics environments — including semantic layers, dimensional models, and metrics-as-a-service frameworks — that standardize KPI definitions and enable data-driven decision-making across a global user base.
• Knowledge of ML platform architecture, MLOps principles, and AI/ML tooling, with the ability to design data infrastructure that supports the full machine learning lifecycle including feature engineering, model governance, and responsible AI practices.
• Comprehensive understanding of global data privacy regulations (GDPR, CCPA, LGPD) and data sovereignty requirements, with demonstrated ability to architect security controls — including access management, classification frameworks, encryption, and audit logging — that protect enterprise data and ensure cross-regional regulatory compliance.
• Skill in operationalizing enterprise data governance programs, including the establishment of data ownership models, stewardship structures, policy frameworks, and metadata management solutions that sustain data integrity at scale.
• Ability to translate complex architecture concepts for executive and business audiences, lead multi-year data and analytics roadmap planning, build compelling business cases for platform investment, and drive adoption of emerging technologies — including generative AI and composable data architectures — aligned to enterprise strategy.
• Navigate highly complex, ambiguous challenges at the intersection of business strategy and technology evaluating competing architectural approaches, platform trade-offs, and cross-regional constraints to design enterprise-wide data solutions that are scalable, future-proof, and aligned to long-term organizational priorities.
• Diagnose and resolves systemic data integrity, quality, and consistency issues spanning multiple source systems, business domains, and geographies applying root cause analysis, MDM principles, and governance frameworks to eliminate data conflicts that would otherwise undermine the reliability of enterprise reporting and decision-making.
• Anticipate and architects solutions for emerging risks at the boundary of data security, privacy regulation, and technology change proactively identifying compliance gaps, sovereignty conflicts, and access vulnerabilities across global platforms before they materialize into organizational, legal, or reputational exposure.
• Resolve the tension between enterprise standardization and regional business flexibility — designing governance models and platform architectures that enforce consistency where it matters most while preserving the agility local markets and business units need to operate effectively and compete.
• Evaluate and integrate rapidly evolving technologies including generative AI, real-time analytics, and composable data architectures, assessing organizational readiness, risk, and business value to make well-reasoned build, buy, or partner decisions that advance the enterprise analytics roadmap without introducing unsustainable technical debt.
• Design and govern a trusted, scalable enterprise data architecture that enables consistent, high-quality data across all global markets, systems, and business domains.
• Deliver a secure, compliant, and future-ready data and analytics platform that accelerates business intelligence, supports AI and machine learning capabilities, and drives measurable competitive advantage.
• Lead the translation of enterprise data strategy into an executable, stakeholder-aligned roadmap that advances data maturity, builds organizational capability, and positions the company to compete through data-driven insight.
• Data quality, accuracy, timeliness, and consistency.
• Portfolio-level project prioritization, selection, and execution across systems.
• Bachelor's degree required in Computer Science, Information Systems, Data Science, Engineering, or a closely related technical discipline.
• 10+ years of progressive experience in data architecture, data engineering, or analytics engineering roles, with demonstrated growth from hands-on technical delivery to enterprise-wide architectural ownership.
• 5+ years of experience designing and governing enterprise data platforms in a global or multi-regional organization, with direct exposure to the complexity of operating across multiple trading blocs, regulatory environments, and business cultures.
• Proven experience leading end-to-end delivery of large-scale, cross-functional data and analytics programs including platform migrations, MDM implementations, and BI modernization initiatives spanning multiple systems and geographies.
• Demonstrated experience building and managing data governance frameworks, including data quality programs, stewardship models, metadata management, and policy enforcement at enterprise scale.
• Deep, hands-on architectural experience with the Microsoft Data Stack (SQL, Azure SQL, Power BI) and modern data integration/lakehouse tooling (e.g., Snowflake, Databricks, SnapLogic, dbt, Azure Data Factory
• 5+ years developing / maintaining data warehouse or enterprise level database management systems
• 5+ years developing data modeling solutions
• 5+ years developing / maintaining enterprise level data intelligence platforms and visualization tools
• 3+ years experience providing technical leadership, mentorship, and architectural oversight to data engineering teams
• 3+ years experience with Agile project delivery

Nice To Haves

• Master's degree preferred in Data Science, Business Intelligence, Information Management, or MBA with a technology concentration — particularly valuable for candidates operating at the intersection of business strategy and enterprise architecture.
• Professional certifications that strengthen candidacy include TOGAF, DAMA CDMP, Cloud platform certifications (AWS/Azure/GCP Data Architect), and modern BI/Data engineering credentials (Databricks, Snowflake, Microsoft Certified Data Analyst)Microsoft Certified Data Analyst Associate
• Microsoft SQL BI Development

Responsibilities

• Enterprise Data Architecture Design & Governance: Define and maintain the global data architecture blueprint, including data models, SaaS integration patterns, and platform standards. Act as the technical design authority, providing architectural oversight and technical guidance to the Data & Analytics Manager and engineering teams during execution.
• Data Platform Strategy & Cloud Infrastructure: Lead the selection, design, and evolution of the enterprise data platform (cloud data warehouses, lakehouses). Design scalable integration architectures capable of ingesting high-volume data from a complex, decentralized enterprise SaaS ecosystem.
• Data Quality, Master Data Management (MDM) & Lineage: Establish and enforce standards for data accuracy, completeness, and traceability to build reliable outputs. Ensure semantic consistency so that LLMs and AI agents can accurately interpret business definitions without hallucination.
• Analytics, BI, & Machine-Consumable Data Enablement: Design the analytics and semantic layers that translate raw data into actionable insight for human leaders (BI tooling) while simultaneously designing the API frameworks and 'Data-as-a-Service' architectures required for AI agents to securely query and act upon enterprise data.
• AI, Machine Learning & Advanced Analytics Integration: Architect the infrastructure, data pipelines, and semantic layers required to support predictive modeling, Generative AI, and Agentic AI initiatives. Design patterns for Retrieval-Augmented Generation (RAG), vector databases, and unstructured data cataloging to ensure the ecosystem is fully 'AI-ready'.
• Data Security, Privacy & Regulatory Compliance: Partner closely with the Cybersecurity COE to embed data protection principles—including Zero Trust access controls (for both human users and non-human AI service accounts), data classification, and retention policies—into every layer of the architecture, ensuring compliance with global regulations (GDPR, CCPA).
• Stakeholder Engagement, Roadmap Planning & Technology Innovation: Serve as a trusted advisor to business and technology leaders, translating strategic priorities into a forward-looking data and analytics roadmap for global stakeholders, and ensures the architecture evolves in step with business growth and market change.