Data Architect - Insurance Data

Arthur J. Gallagher · Rolling Meadows

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Welcome to Gallagher - a global community of people who bring bold ideas, deep expertise, and a shared commitment to doing what’s right. We help clients navigate complexity with confidence by empowering businesses, communities, and individuals to thrive. At Gallagher, you’ll find more than a job; you’ll find a culture built on trust, driven by collaboration, and sustained by the belief that we’re better together. Whether you join us in a client-facing role or as part of our brokerage division, our benefits and HR consulting division, or our corporate team, you’ll have the opportunity to grow your career, make an impact, and be part of something bigger. Experience a workplace where you’re encouraged to be yourself, supported to succeed, and inspired to keep learning. That’s what it means to live The Gallagher Way.

The Data Architect is a hands-on technical leader responsible for designing the data architecture, models, standards, and patterns that enable scalable, trusted, and reusable enterprise data solutions at Gallagher.

You will lead the architecture and design of complex data initiatives spanning operational, analytical, master, and reference data. This includes defining end-to-end data flows, developing conceptual and logical data models, establishing integration and modeling patterns, evaluating solution designs, and ensuring data platforms are designed for scalability, reliability, governance, security, and long-term maintainability.

This is an individual contributor role with technical leadership responsibilities and no direct people management. You will work closely with data engineers, solution and enterprise architects, product teams, analytics, data science, governance, business stakeholders, and technology teams to translate business capabilities and data requirements into practical enterprise data architectures.

How you'll make an impact

Data Architecture & Modeling

• Design end-to-end data architectures spanning source systems, ingestion, transformation, integration, master data, data lakes, warehouses, and downstream consumption.
• Develop and maintain conceptual and logical data models that accurately represent business domains, entities, relationships, and data requirements.
• Define reusable data architecture and modeling patterns that promote consistency across domains and technology platforms.
• Establish clear source-to-target architectures and data flows, including system-of-record, mastering, distribution, and consumption patterns.
• Guide the design of analytical, operational, master, and reference data solutions based on business and technical requirements.
• Define architecture for structured, semi-structured, and unstructured enterprise data.
• Ensure designs support scalability, interoperability, performance, reliability, security, and maintainability.

Technical & Architecture Leadership

• Lead architecture and technical design for complex, cross-domain data initiatives.
• Translate business requirements and capabilities into practical and scalable data architectures.
• Establish and promote standards for data architecture, modeling, integration, metadata, lineage, naming, documentation, and design.
• Conduct architecture and design reviews and ensure solutions align with enterprise standards and target-state architecture.
• Provide architectural direction to data engineering teams throughout design and implementation.
• Identify architectural risks, dependencies, technical debt, and opportunities to simplify or modernize existing solutions.
• Influence technical decisions across teams without direct management responsibility.
• Mentor engineers and other technical professionals on data architecture and modeling practices.

Data Platform & Integration Architecture

• Define architecture patterns across modern cloud data platforms, including Snowflake, Azure, Databricks, ADLS Gen2, Azure Data Factory, and SQL-based platforms.
• Design scalable data lake, data warehouse, data integration, and data product architectures.
• Define patterns for batch, near-real-time, API, event-driven, and file-based data integration where appropriate.
• Partner with engineering teams to ensure architecture can be implemented efficiently and operated reliably in production.
• Define patterns that reduce point-to-point integrations, duplicate data processing, unnecessary data movement, and tightly coupled solutions.
• Evaluate existing platforms and solutions and develop target-state and transition architectures to support modernization.
• Ensure architecture considers performance, resilience, recoverability, observability, and cloud cost.

Data Governance & Management

• Embed data governance requirements into data architecture and solution design.
• Define architecture patterns supporting data ownership, data quality, metadata, lineage, security, privacy, retention, and access management.
• Partner with data governance and business teams to establish consistent definitions and models for critical enterprise data.
• Support master and reference data management strategies, including definition of authoritative sources, mastering, reconciliation, and distribution patterns.
• Ensure architecture clearly documents data movement, transformation, ownership, and consumption.
• Promote discoverable, governed, and reusable enterprise data products.

Business, Engineering & AI Partnership

• Partner with business, product, analytics, data science, security, engineering, governance, and technology teams to develop data solutions aligned to business priorities.
• Facilitate architecture discussions and drive decisions across teams with different technical and business perspectives.
• Communicate architecture decisions, risks, dependencies, and tradeoffs clearly to both technical and non-technical stakeholders.
• Build data foundations that support analytics, AI, machine learning, and other advanced data use cases.
• Ensure AI and analytics solutions are supported by well-designed, governed, and trusted data foundations.
• Balance strategic architecture direction with practical delivery needs, helping teams move toward the target state incrementally.

Required:

• Typically requires a University Degree and minimum 3 years of prior relevant experience, or equivalent experience.
• Our ideal candidate will have a Bachelor's degree in Computer Science, Information Technology, Engineering, Data Management, or related field.
• 7+ years of experience working with enterprise data platforms, data engineering, data modeling, or data architecture, including significant experience in a data architecture or senior technical design role.
• Proven experience designing enterprise data architectures across complex data ecosystems.
• Strong experience with conceptual and logical data modeling, including defining business entities, relationships, domains, and canonical models.
• Strong understanding of data lake, data warehouse, lakehouse, data integration, master data, and modern data platform concepts.
• Experience designing solutions on modern cloud data platforms such as Snowflake, Azure, Databricks, ADLS Gen2, Azure Data Factory, and SQL-based platforms.
• Strong understanding of ETL/ELT, APIs, event-driven integration, and other enterprise data integration patterns.
• Experience establishing data architecture, modeling, integration, and documentation standards.
• Strong understanding of data governance, metadata, lineage, data quality, security, and data ownership concepts.
• Ability to translate complex business requirements into clear data architectures and technical designs.
• Ability to lead architecture initiatives and influence engineering and technology teams without direct management authority.
• Strong analytical, problem-solving, communication, and stakeholder-management skills. 

At Gallagher, we believe supporting our colleagues goes far beyond the role itself. For more information, visit our Benefits page.

• Competitive compensation
• Comprehensive benefits programs designed to support your well-being 
• Career development opportunities and ongoing learning 
• A collaborative, people-first culture with accessible leadership 
• The opportunity to do meaningful work with global reach and local impact 

At Gallagher, we are dedicated to building an inclusive and authentic workplace. If your past experience doesn’t align perfectly, we encourage you to join our Talent Community to stay connected to additional career opportunities. At times, we will consider transferable skills from previous roles.

Gallagher is an affirmative action/equal opportunity employer (Minorities/Females/Veterans/Disabled)

The Data Architect is a hands-on technical leader responsible for designing the data architecture, models, standards, and patterns that enable scalable, trusted, and reusable enterprise data solutions at Gallagher.

You will lead the architecture and design of complex data initiatives spanning operational, analytical, master, and reference data. This includes defining end-to-end data flows, developing conceptual and logical data models, establishing integration and modeling patterns, evaluating solution designs, and ensuring data platforms are designed for scalability, reliability, governance, security, and long-term maintainability.

This is an individual contributor role with technical leadership responsibilities and no direct people management. You will work closely with data engineers, solution and enterprise architects, product teams, analytics, data science, governance, business stakeholders, and technology teams to translate business capabilities and data requirements into practical enterprise data architectures.

How you'll make an impact

Data Architecture & Modeling

• Design end-to-end data architectures spanning source systems, ingestion, transformation, integration, master data, data lakes, warehouses, and downstream consumption.
• Develop and maintain conceptual and logical data models that accurately represent business domains, entities, relationships, and data requirements.
• Define reusable data architecture and modeling patterns that promote consistency across domains and technology platforms.
• Establish clear source-to-target architectures and data flows, including system-of-record, mastering, distribution, and consumption patterns.
• Guide the design of analytical, operational, master, and reference data solutions based on business and technical requirements.
• Define architecture for structured, semi-structured, and unstructured enterprise data.
• Ensure designs support scalability, interoperability, performance, reliability, security, and maintainability.

Technical & Architecture Leadership

• Lead architecture and technical design for complex, cross-domain data initiatives.
• Translate business requirements and capabilities into practical and scalable data architectures.
• Establish and promote standards for data architecture, modeling, integration, metadata, lineage, naming, documentation, and design.
• Conduct architecture and design reviews and ensure solutions align with enterprise standards and target-state architecture.
• Provide architectural direction to data engineering teams throughout design and implementation.
• Identify architectural risks, dependencies, technical debt, and opportunities to simplify or modernize existing solutions.
• Influence technical decisions across teams without direct management responsibility.
• Mentor engineers and other technical professionals on data architecture and modeling practices.

Data Platform & Integration Architecture

• Define architecture patterns across modern cloud data platforms, including Snowflake, Azure, Databricks, ADLS Gen2, Azure Data Factory, and SQL-based platforms.
• Design scalable data lake, data warehouse, data integration, and data product architectures.
• Define patterns for batch, near-real-time, API, event-driven, and file-based data integration where appropriate.
• Partner with engineering teams to ensure architecture can be implemented efficiently and operated reliably in production.
• Define patterns that reduce point-to-point integrations, duplicate data processing, unnecessary data movement, and tightly coupled solutions.
• Evaluate existing platforms and solutions and develop target-state and transition architectures to support modernization.
• Ensure architecture considers performance, resilience, recoverability, observability, and cloud cost.

Data Governance & Management

• Embed data governance requirements into data architecture and solution design.
• Define architecture patterns supporting data ownership, data quality, metadata, lineage, security, privacy, retention, and access management.
• Partner with data governance and business teams to establish consistent definitions and models for critical enterprise data.
• Support master and reference data management strategies, including definition of authoritative sources, mastering, reconciliation, and distribution patterns.
• Ensure architecture clearly documents data movement, transformation, ownership, and consumption.
• Promote discoverable, governed, and reusable enterprise data products.

Business, Engineering & AI Partnership

• Partner with business, product, analytics, data science, security, engineering, governance, and technology teams to develop data solutions aligned to business priorities.
• Facilitate architecture discussions and drive decisions across teams with different technical and business perspectives.
• Communicate architecture decisions, risks, dependencies, and tradeoffs clearly to both technical and non-technical stakeholders.
• Build data foundations that support analytics, AI, machine learning, and other advanced data use cases.
• Ensure AI and analytics solutions are supported by well-designed, governed, and trusted data foundations.
• Balance strategic architecture direction with practical delivery needs, helping teams move toward the target state incrementally.

Required:

• Typically requires a University Degree and minimum 3 years of prior relevant experience, or equivalent experience.
• Our ideal candidate will have a Bachelor's degree in Computer Science, Information Technology, Engineering, Data Management, or related field.
• 7+ years of experience working with enterprise data platforms, data engineering, data modeling, or data architecture, including significant experience in a data architecture or senior technical design role.
• Proven experience designing enterprise data architectures across complex data ecosystems.
• Strong experience with conceptual and logical data modeling, including defining business entities, relationships, domains, and canonical models.
• Strong understanding of data lake, data warehouse, lakehouse, data integration, master data, and modern data platform concepts.
• Experience designing solutions on modern cloud data platforms such as Snowflake, Azure, Databricks, ADLS Gen2, Azure Data Factory, and SQL-based platforms.
• Strong understanding of ETL/ELT, APIs, event-driven integration, and other enterprise data integration patterns.
• Experience establishing data architecture, modeling, integration, and documentation standards.
• Strong understanding of data governance, metadata, lineage, data quality, security, and data ownership concepts.
• Ability to translate complex business requirements into clear data architectures and technical designs.
• Ability to lead architecture initiatives and influence engineering and technology teams without direct management authority.
• Strong analytical, problem-solving, communication, and stakeholder-management skills. 

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