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Rexel USA

Distributes electrical supplies to US customers

Data Architect

Data ManagementConfirmed live in the last 24 hours
Full-timeÎle-de-France, France

About the Job

Requirements

  • Engineering degree or equivalent, with at least 10 years of professional experience.
  • Proven experience delivering large-scale data projects.
  • Significant experience working with offshore teams is mandatory; experience with teams in India is a plus.
  • Expert knowledge of project management methodologies and tools.
  • Strong expertise in information technology systems and architectures.
  • Extensive expertise in data modeling principles and methodologies, including conceptual, logical, and physical data models.
  • Strong expertise in both dimensional and operational data modeling.
  • Solid experience in software development and SQL.
  • Hands-on experience with data catalog tools such as Atlan.
  • Proven experience with Snowflake is mandatory.
  • Excellent leadership skills and proven experience collaborating with cross-functional teams.
  • Ability and seniority to challenge country business teams and question existing processes when relevant.
  • Pragmatic, solution-oriented mindset, with the ability to address complex issues and translate business needs into effective solutions.
  • Strong change management skills, with the ability to drive transformation and successfully engage and onboard business teams.

Responsibilities

  • Drive the evolution of the enterprise data architecture by partnering with data citizens, country IT teams, and business stakeholders to implement data strategies, design data flows, and develop conceptual, logical, and physical data models.
  • Act as a subject-matter expert and reference point for data modeling, ensuring consistency, reusability, and alignment with existing data assets and enterprise architecture principles.
  • Define and govern data modeling, data integration, data quality, metadata management, and design standards across the organization.
  • Establish best practices for data products, data domains, data contracts, APIs, event-driven integrations, and self-service analytics.
  • Set up and lead architecture and governance meetings with country IT services, ensuring clear decisions, ownership, and a defined path forward.
  • Keep up to date with industry trends and evaluate emerging technologies in cloud data platforms, artificial intelligence, machine learning, analytics, automation, and data governance.
  • Contribute to defining an AI-ready data architecture, ensuring that data is discoverable, trusted, secure, well-governed, and suitable for advanced analytics and AI use cases.
  • Design and maintain the enterprise data model, owning its lifecycle, documentation, dependencies, and end-to-end lineage.
  • Design and evolve cloud-native data platforms, including data lakes, lakehouses, operational data stores, curated data layers, data marts, and analytical data products.
  • Define scalable and resilient data architectures using modern storage, processing, integration, and analytics technologies rather than relying solely on traditional relational database management systems.
  • Identify the architecture, infrastructure, interfaces, and integration patterns required to connect operational systems, software-as-a-service applications, APIs, files, and other enterprise data sources.
  • Design and support automated batch and real-time data pipelines, including orchestration, monitoring, data validation, error handling, and recovery mechanisms.
  • Establish data observability practices covering pipeline health, freshness, completeness, accuracy, availability, schema changes, lineage, and service-level expectations.
  • Support implementation and adoption of enterprise data cataloging and metadata management tools, ensuring critical data assets are documented, searchable, classified, and assigned to accountable owners.
  • Collaborate with data governance teams to implement data ownership, business glossaries, data classification, access policies, retention rules, and regulatory requirements.
  • Design data structures and interfaces that support reporting, advanced analytics, machine learning, generative AI, and other data-driven applications.
  • Ensure data models and platform components support AI and machine learning use cases, including feature management, training datasets, model inputs, data lineage, explainability, and responsible AI principles.
  • Identify and address security, privacy, compliance, performance, scalability, and cost-management considerations across the data platform.
  • Contribute to designing semantic layers and reusable analytical models that enable consistent business definitions across dashboards, analytics, and AI applications.
  • Remain hands-on in conceptual, logical, and physical data modeling, data architecture, and platform design.
  • Develop, maintain, and document standards and methodologies covering data architecture, data modeling, data integration, data quality, metadata, observability, and platform engineering.
  • Maintain onboarding documentation, architecture guidelines, reference patterns, playbooks, and knowledge bases for internal teams and implementation partners.
  • Promote common data definitions, reusable patterns, and consistent implementation practices across countries and business domains.
  • Provide expert judgment on data model efficiency, scalability, maintainability, performance, and suitability for analytics and AI use cases.
  • Define and monitor validation processes to ensure data models, pipelines, data products, and platform components meet agreed quality and governance standards.
  • Support architecture reviews, design authority processes, and technical assessments for new data initiatives.
  • Encourage a culture of data ownership, data literacy, continuous improvement, and engineering excellence across the organization.
  • Share knowledge with solution architects, data engineers, analytics teams, data stewards, and business stakeholders through workshops, coaching, and communities of practice.
  • Drive development of the data platform, define its technological roadmap, ensure continuous improvement, and connect the platform with AI and advanced analytics solutions.
  • Establish close links between central Hub teams and IT managers in several European countries, acting as a data platform ambassador to support rapid adoption and value generation for internal customers.

Desired Qualifications

  • Good knowledge of Power BI.
  • Experience with dbt.
  • Experience working with teams in India.

About the Company

Rexel USA

Distributes electrical supplies to US customers

Rexel USA distributes electrical and related energy and communication products to millions of US customers, backed by the Rexel Group’s global footprint. It combines product sourcing with services like electrical design input, lighting upgrades, alternative energy solutions, and supply-chain optimization to help customers complete projects efficiently. The company differentiates itself by leveraging scale, a broad brand portfolio, and hands-on programs that pair products with practical, customer-focused solutions. Its goal is to help customers thrive by expanding brands and services while delivering value to shareholders.

Company size
1,001-5,000
Company stage
Not listed
Total funding
Not listed
Headquarters
Dallas, TX
Founded
2006

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