Overview In this role you will lead the design, delivery, and operation of Gallagher's data platform to enable analytics, reporting, and AI. You'll mentor a cross-functional data engineering team, align work with business goals, and shape the data & integration roadmap. You'll manage production reliability, cost, and collaboration across time zones, balancing hands-on review with people leadership. This is a builder-manager role focused on scalable data foundations and AI-enabled capabilities that impact decision making and product outcomes. You'll work in a collaborative, cross-functional environment to drive measurable data-driven impact across the organization. Compensation / BenefitsCompetitive compensationComprehensive benefits programsCareer development and ongoing learningCollaborative, people-first cultureGlobal reach with local impactSupportive leadership and employee well-being ResponsibilitiesProvide architectural direction and oversee requirements gathering, scope definition, and technical design for data and integration workflowsSet and enforce engineering standards including code reviews, ETL/ELT frameworks, testing, and documentationManage operational health of production data systems: SLAs, monitoring, incident response, and post-incident reviewsLead platform and tooling decisions including capacity planning, vendor evaluation, and cloud cost managementHire, onboard, coach, and retain data engineers across locations; manage performance and career developmentCoordinate across time zones to ensure effective communication and collaboration between development and support teamsPartner with data science and analytics leaders to align engineering capacity with AI/BI prioritiesAct as liaison between technical teams and business users to ensure data solutions meet business needsBuild and maintain ETL/ELT pipelines and data integrations using cloud-native tools (Azure Data Factory, Databricks, Snowflake)Own data modeling standards and semantic layer for analytics/BI consumptionImplement data quality, observability, lineage, and cataloging practicesDrive process improvements, automation, and scalable infrastructure to reduce manual work and costDesign and operate data foundations for AI including feature pipelines and training datasetsEvaluate and integrate AI capabilities and establish governance around AI use of enterprise data Key requirementsBachelor in Information Technology5+ years leading a technical team7+ years data engineering experience with Snowflake, Azure Data Factory, ADLS Gen 2, Azure Functions, Databricks, Synapse, SQL ServerExperience delivering data foundations for AI/ML workloads (feature pipelines, training datasets, embeddings, model deployment support)Governance of AI use of sensitive data (access, masking, lineage, auditability)Experience with Data Lake architectures on Microsoft AzureExperience ETL for Data Warehouse and BI solutionsStrong coding skills with modular, robust, parameterized ETL/ELT code and adherence to standardsKnowledge of DevOps, CI/CD, and Infrastructure as CodeExperience in agile environments and root cause analysis for data processesExcellent communication and stakeholder management across levelsAbility to navigate ambiguity and deliver for multiple teams and prioritiesProven track record of delivering on data systems and integrations across projectsStrong communication and interpersonal skillsCollaboration and stakeholder managementLeadership and mentoringSnowflakeAzure Data FactoryADLS Gen 2
Not specified in the original listing.
Not specified in the original listing.
