Data Architect - Insurance Data

Gallagher•Rolling Meadows, IL

About The Position

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. The role leads architecture and design for complex initiatives across operational, analytical, master, and reference data, including end-to-end data flows, conceptual and logical data models, integration and modeling patterns, solution evaluations, and designs that support scalability, reliability, governance, security, and maintainability. This is an individual-contributor role with technical leadership responsibilities and no direct people management. The Data Architect works 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. The role also supports analytics, AI, and machine-learning use cases with governed, trusted data foundations.

Requirements

Typically requires a university degree and a minimum of 3 years of prior relevant experience, or equivalent experience.
Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Management, or a related field is specified for the ideal candidate.
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 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.

Responsibilities

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 for business domains, entities, relationships, and data requirements.
Define reusable data architecture and modeling patterns, 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 and architecture for structured, semi-structured, and unstructured data.
Ensure designs support scalability, interoperability, performance, reliability, security, and maintainability.
Lead architecture and technical design for complex, cross-domain data initiatives and translate business requirements into scalable data architectures.
Establish standards for data architecture, modeling, integration, metadata, lineage, naming, documentation, and design.
Conduct architecture and design reviews to ensure alignment with enterprise standards and target-state architecture.
Provide architectural direction to data engineering teams through design and implementation; identify risks, dependencies, technical debt, and modernization opportunities.
Influence technical decisions across teams and mentor engineers and other technical professionals on data architecture and modeling practices.
Define architecture patterns across modern cloud data platforms, data lakes, warehouses, integrations, and data products.
Define patterns for batch, near-real-time, API, event-driven, and file-based integration; partner with engineering to support efficient implementation and reliable production operations.
Reduce point-to-point integrations, duplicate processing, unnecessary data movement, and tightly coupled solutions; evaluate existing platforms and develop target-state and transition architectures.
Ensure architecture accounts for performance, resilience, recoverability, observability, and cloud cost.
Embed governance requirements into architecture, including ownership, data quality, metadata, lineage, security, privacy, retention, and access management.
Partner with governance and business teams to establish consistent definitions and models for critical enterprise data.
Support master and reference data strategies, including authoritative sources, mastering, reconciliation, and distribution patterns.
Document data movement, transformation, ownership, and consumption, and promote discoverable, governed, reusable enterprise data products.
Partner with business, product, analytics, data science, security, engineering, governance, and technology teams to develop solutions aligned with business priorities.
Facilitate architecture discussions, drive decisions across teams, and communicate decisions, risks, dependencies, and tradeoffs to technical and non-technical stakeholders.
Build data foundations for analytics, AI, and machine learning, balancing strategic direction with practical delivery needs and incremental progress toward target state.