Introduction 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. Overview The Data Engineering Manager owns the design, delivery, and operation of the data platform that powers analytics, reporting, and AI at Gallagher. You will lead and mentor data engineers, ensuring alignment with business goals and fostering collaboration across multiple teams, systems, and products. You will also oversee deliverables and provide ongoing support to ensure project success and operational excellence. This is a builder-manager role. You will spend most of your time growing and directing the team, shaping the roadmap, and negotiating priorities with the business - while staying technical enough to review designs and code, unblock engineers, and manage multiple priorities. Do you find the prospect of optimizing or even re-designing our company’s integration and data architecture to support our next generation of products and data initiatives most exciting? We really should explore together. How you'll make an impact How You'll Make an Impact Leadership and Strategy Lead requirements gathering, scope definition, and technical design for data and integration workflows, providing strategic and architectural direction to the team. Set and enforce engineering standards - code review, ETL/ELT frameworks, testing, documentation, and definition of done. Proactively identify risks in data initiatives and develop mitigation strategies. Own the operational health of production data systems and integrations: SLAs, monitoring and alerting, incident response, and blameless post-incident review. Contribute to platform and tooling selection, capacity planning, vendor evaluation, and budget forecasting, including cloud and platform consumption costs. People and Team Management Hire, onboard, coach, and retain data engineers across onsite and offshore locations; own performance management, career development, and succession planning. Build a team operating model that scales - clear ownership, sustainable on-call and support rotation, and reduced single points of failure. Provide leadership, direction, and coordination for development and support teams across time zones, ensuring effective communication and collaboration. Partner with data science and analytics leaders to align engineering capacity with modeling, reporting, and AI priorities. Stakeholder Management Act as the liaison between technical teams and business users, ensuring data solutions meet real business needs and that concerns are addressed directly. Manage deliverables across multiple teams and products, ensuring timely completion and alignment with business priorities. Communicate platform health, delivery status, and roadmap changes transparently to both technical and non-technical audiences. Data Platform and Engineering Build and maintain the infrastructure required for optimal ETL/ELT pipelines, ingesting data from a wide variety of sources using cloud-native tooling such as Azure Data Factory, Databricks, and Snowflake. Construct and maintain enterprise-level integrations across Snowflake, Azure Synapse, Azure SQL, and SQL Server, including a deliberate plan to retire overlapping or legacy platforms. Own data modeling standards (dimensional, Data Vault, or equivalent) and the semantic layer that analytics and BI tools consume. Establish data quality, observability, lineage, and cataloging practices so consumers can assess trust in a dataset without asking a human. Seek out, design, and implement process improvements: automating manual work, optimizing data delivery, and re-designing infrastructure for greater scalability and lower cost. Build the data tools and curated datasets that analytics and data science teams depend on, including the reporting foundations behind our BI platform. Drive down recurring support burden through automation, better instrumentation, and permanent fixes rather than repeated manual intervention. AI and Advanced Analytics Enablement Design and operate the data foundations for AI: feature pipelines, training and evaluation datasets, and reproducible, point-in-time-correct data for modeling. Evaluate and integrate platform AI capabilities (for example, in-warehouse LLM functions, managed model endpoints, and agent orchestration frameworks) and make build-versus-buy recommendations. Establish guardrails for AI use of enterprise data: PII handling and masking, access controls, prompt and response logging, output evaluation, and auditability aligned with internal governance and regulatory requirements. Champion responsible, effective use of AI within the engineering team itself - coding assistants, automated testing and documentation, and pipeline generation - with measured impact on delivery throughput and quality. About You Required: A relevant technical BS Degree in Information Technology. Proven experience in leading cross-functional teams, managing business users, and driving alignment between technical and business objectives. 5+ years of experience leading a technical team. 7+ years of data engineering experience leveraging technologies such as Snowflake, Azure Data Factory, ADLS Gen 2, Azure Functions, Databricks, Synapse, SQL Server. Demonstrated experience delivering data foundations for AI or machine learning workloads - for example feature pipelines, training datasets, embeddings and vector search, model deployment support, or LLM-based applications on enterprise data. Practical understanding of how to govern AI use of sensitive data: access control, masking, lineage, and auditability. Understanding the pros and cons, and best practices of implementing Data Lake, using Microsoft Azure Data Lake Storage. Experience structuring Data Lake for reliability, security, and performance. Experience implementing ETL for Data Warehouse and Business Intelligence solutions. Skills to read and write effective, modular, dynamic, parameterized, and robust code, establish and follow already established code standards, and ETL framework. Strong analytical, problem-solving, and troubleshooting abilities. Good understanding of unit testing, software change management, and software release management. Knowledge of DevOps processes (including CI/CD) and Infrastructure as Code fundamentals. Experience performing root cause analysis on data and processes to answer specific business questions and identify opportunities for improvement. Experience working within an agile team. Exceptional communication and interpersonal skills, with the ability to influence and guide stakeholders at all levels. Demonstrated ability to manage relationships with business users, address their concerns, and ensure their needs are met through effective communication and collaboration. Experience in navigating ambiguity and uncertainty in projects, with a track record of delivering successful outcomes. Proven experience managing deliverables across multiple teams and providing ongoing support for data systems and integrations. Compensation and benefits 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) How You'll Make an Impact Leadership and Strategy Lead requirements gathering, scope definition, and technical design for data and integration workflows, providing strategic and architectural direction to the team. Set and enforce engineering standards - code review, ETL/ELT frameworks, testing, documentation, and definition of done. Proactively identify risks in data initiatives and develop mitigation strategies. Own the operational health of production data systems and integrations: SLAs, monitoring and alerting, incident response, and blameless post-incident review. Contribute to platform and tooling selection, capacity planning, vendor evaluation, and budget forecasting, including cloud and platform consumption costs. People and Team Management Hire, onboard, coach, and retain data engineers across onsite and offshore locations; own performance management, career development, and succession planning. Build a team operating model that scales - clear ownership, sustainable on-call and support rotation, and reduced single points of failure. Provide leadership, direction, and coordination for development and support teams across time zones, ensuring effective communication and collaboration. Partner with data science and analytics leaders to align engineering capacity with modeling, reporting, and AI priorities. Stakeholder Management Act as the liaison between technical teams and business users, ensuring data solutions meet real business needs and that concerns are addressed directly. Manage deliverables across multiple teams and products, ensuring timely completion and alignment with business priorities. Communicate platform health, delivery status, and roadmap changes transparently to both technical and non-technical audiences. Data Platform and Engineering Build and maintain the infrastructure required for optimal ETL/ELT pipelines, ingesting data from a wide variety of sources using cloud-native tooling such as Azure Data Factory, Databricks, and Snowflake. Construct and maintain enterprise-level integrations across Snowflake, Azure Synapse, Azure SQL, and SQL Server, including a deliberate plan to retire overlapping or legacy platforms. Own data modeling standards (dimensional, Data Vault, or equivalent) and the semantic layer that analytics and BI tools consume. Establish data quality, observability, lineage, and cataloging practices so consumers can assess trust in a dataset without asking a human. Seek out, design, and implement process improvements: automating manual work, optimizing data delivery, and re-designing infrastructure for greater scalability and lower cost. Build the data tools and curated datasets that analytics and data science teams depend on, including the reporting foundations behind our BI platform. Drive down recurring support burden through automation, better instrumentation, and permanent fixes rather than repeated manual intervention. AI and Advanced Analytics Enablement Design and operate the data foundations for AI: feature pipelines, training and evaluation datasets, and reproducible, point-in-time-correct data for modeling. Evaluate and integrate platform AI capabilities (for example, in-warehouse LLM functions, managed model endpoints, and agent orchestration frameworks) and make build-versus-buy recommendations. Establish guardrails for AI use of enterprise data: PII handling and masking, access controls, prompt and response logging, output evaluation, and auditability aligned with internal governance and regulatory requirements. Champion responsible, effective use of AI within the engineering team itself - coding assistants, automated testing and documentation, and pipeline generation - with measured impact on delivery throughput and quality. Required: A relevant technical BS Degree in Information Technology. Proven experience in leading cross-functional teams, managing business users, and driving alignment between technical and business objectives. 5+ years of experience leading a technical team. 7+ years of data engineering experience leveraging technologies such as Snowflake, Azure Data Factory, ADLS Gen 2, Azure Functions, Databricks, Synapse, SQL Server. Demonstrated experience delivering data foundations for AI or machine learning workloads - for example feature pipelines, training datasets, embeddings and vector search, model deployment support, or LLM-based applications on enterprise data. Practical understanding of how to govern AI use of sensitive data: access control, masking, lineage, and auditability. Understanding the pros and cons, and best practices of implementing Data Lake, using Microsoft Azure Data Lake Storage. Experience structuring Data Lake for reliability, security, and performance. Experience implementing ETL for Data Warehouse and Business Intelligence solutions. Skills to read and write effective, modular, dynamic, parameterized, and robust code, establish and follow already established code standards, and ETL framework. Strong analytical, problem-solving, and troubleshooting abilities. Good understanding of unit testing, software change management, and software release management. Knowledge of DevOps processes (including CI/CD) and Infrastructure as Code fundamentals. Experience performing root cause analysis on data and processes to answer specific business questions and identify opportunities for improvement. Experience working within an agile team. Exceptional communication and interpersonal skills, with the ability to influence and guide stakeholders at all levels. Demonstrated ability to manage relationships with business users, address their concerns, and ensure their needs are met through effective communication and collaboration. Experience in navigating ambiguity and uncertainty in projects, with a track record of delivering successful outcomes. Proven experience managing deliverables across multiple teams and providing ongoing support for data systems and integrations.
