Arthur J. Gallagher & Co.

Global insurance brokerage, risk management, consulting

Data Engineering Lead

Data EngineeringConfirmed live in the last 24 hours

Full-timeRolling Meadows, IL, USANo salary listedIn-person

About the Job

Requirements

A bachelor's degree in Computer Science, Information Technology, Engineering, or a related field is required.
At least 7 years of hands-on data engineering experience, including at least 3 years in a technical lead or senior engineering role, is required.
Strong hands-on experience with Snowflake, Azure Data Factory, Databricks or Spark, ADLS Gen2, and SQL is required.
Strong programming experience with Python, Scala, or an equivalent language is required.
Proven experience designing and implementing enterprise ETL/ELT pipelines and data integrations is required.
A strong understanding of data lake, data warehouse, and data modeling concepts is required.
Experience with production troubleshooting, performance tuning, data quality, and pipeline reliability is required.
Experience with CI/CD and modern software engineering practices is required.
The ability to lead technical initiatives and influence engineers without direct management authority is required.
Strong problem-solving and communication skills are required.

Responsibilities

Design, develop, test, deploy, and support scalable ETL/ELT pipelines and data integrations.
Write production-quality SQL and Python, Scala, or Spark code for data ingestion, transformation, and processing.
Build reusable, modular, and reliable data engineering frameworks and pipelines.
Work across Snowflake, Azure Data Factory, Databricks, Spark, ADLS Gen2, Azure Synapse, and SQL Server or Azure SQL.
Develop curated datasets and data products supporting analytics, reporting, data science, and AI.
Troubleshoot complex data, pipeline, integration, and performance issues.
Optimize pipelines, queries, compute, and storage for performance, reliability, and cost.
Lead technical design and implementation of complex data engineering initiatives.
Translate business requirements into practical, scalable technical solutions.
Establish engineering standards for coding, testing, data quality, documentation, security, and deployment.
Conduct design and code reviews and establish reusable engineering patterns.
Mentor engineers and provide technical direction without direct management responsibility.
Identify technical risks, dependencies, and opportunities to simplify or modernize existing solutions.
Design scalable data lake, warehouse, and integration solutions using appropriate data modeling practices.
Build data quality, monitoring, logging, and observability into production pipelines.
Own the technical reliability of data pipelines and integrations.
Lead root-cause analysis and implement permanent fixes for production issues.
Improve CI/CD, release management, automation, and operational support practices.
Help reduce technical debt, manual processes, duplicate integrations, and single points of failure.
Partner with business, analytics, data science, security, and technology teams to deliver data solutions aligned to business priorities.
Communicate technical decisions, risks, and tradeoffs clearly to technical and non-technical stakeholders.
Build data foundations that support AI and advanced analytics, including structured and unstructured data use cases.
Apply appropriate security, governance, and access controls to enterprise data.

Desired Qualifications

Experience modernizing legacy data platforms and integrations is preferred.
Experience with data observability, lineage, metadata, or governance is preferred.
Experience supporting AI/ML, RAG, semantic search, or other advanced analytics workloads is preferred.
Experience optimizing cloud data platforms for performance and cost is preferred.
Experience working with distributed engineering teams is preferred.

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 Lead is a hands-on technical leader responsible for designing, building, and supporting the data platform that powers analytics, reporting, and AI at Gallagher.

You will lead complex data engineering initiatives while remaining deeply involved in the technology. This includes developing data pipelines, designing integrations, troubleshooting production issues, optimizing performance, reviewing code, and establishing engineering standards.

This is an individual contributor role with technical leadership responsibilities and no direct people management. You will work closely with data engineers, analytics, data science, business, and technology teams to translate business needs into scalable and reliable data solutions.

How you'll make an impact

Hands-On Data Engineering

Design, develop, test, deploy, and support scalable ETL/ELT pipelines and data integrations.
Write production-quality SQL and Python/Scala/Spark code for data ingestion, transformation, and processing.
Build reusable, modular, and reliable data engineering frameworks and pipelines.
Work across Snowflake, Azure Data Factory, Databricks, Spark, ADLS Gen2, Azure Synapse, and SQL Server/Azure SQL.
Develop curated datasets and data products supporting analytics, reporting, data science, and AI.
Troubleshoot complex data, pipeline, integration, and performance issues.
Optimize pipelines, queries, compute, and storage for performance, reliability, and cost.

Technical Leadership

Lead technical design and implementation of complex data engineering initiatives.
Translate business requirements into practical, scalable technical solutions.
Establish engineering standards for coding, testing, data quality, documentation, security, and deployment.
Conduct design and code reviews and establish reusable engineering patterns.
Mentor engineers and provide technical direction without direct management responsibility.
Identify technical risks, dependencies, and opportunities to simplify or modernize existing solutions.

Data Platform & Reliability

Design scalable data lake, warehouse, and integration solutions using appropriate data modeling practices.
Build data quality, monitoring, logging, and observability into production pipelines.
Own the technical reliability of data pipelines and integrations.
Lead root-cause analysis and implement permanent fixes for production issues.
Improve CI/CD, release management, automation, and operational support practices.
Help reduce technical debt, manual processes, duplicate integrations, and single points of failure.

Business & AI Partnership

Partner with business, analytics, data science, security, and technology teams to deliver data solutions aligned to business priorities.
Communicate technical decisions, risks, and tradeoffs clearly to technical and non-technical stakeholders.
Build data foundations that support AI and advanced analytics, including structured and unstructured data use cases.
Apply appropriate security, governance, and access controls to enterprise data.

About You

Required Qualifications

Bachelor's degree in Computer Science, Information Technology, Engineering, or related field.
7+ years of hands-on data engineering experience, including 3+ years in a technical lead or senior engineering role.
Strong hands-on experience with Snowflake, Azure Data Factory, Databricks/Spark, ADLS Gen2, and SQL.
Strong programming experience with Python, Scala, or equivalent.
Proven experience designing and implementing enterprise ETL/ELT pipelines and data integrations.
Strong understanding of data lake, data warehouse, and data modeling concepts.
Experience with production troubleshooting, performance tuning, data quality, and pipeline reliability.
Experience with CI/CD and modern software engineering practices.
Ability to lead technical initiatives and influence engineers without direct management authority.
Strong problem-solving and communication skills.

Preferred Qualifications

Experience modernizing legacy data platforms and integrations.
Experience with data observability, lineage, metadata, or governance.
Experience supporting AI/ML, RAG, semantic search, or other advanced analytics workloads.
Experience optimizing cloud data platforms for performance and cost.
Experience working with distributed engineering teams.

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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)

About the Company

Arthur J. Gallagher & Co.

Global insurance brokerage, risk management, consulting

Arthur J. Gallagher & Co. is a global insurance brokerage and risk management firm. It helps businesses, public entities, and individuals manage risk and obtain insurance through three segments: brokerage, risk management, and corporate. The brokerage unit offers a full range of retail and wholesale insurance brokerage and consulting services to match clients with coverage. The risk management unit handles contract claim settlements, claim administration, loss control consulting, and risk management information services to reduce exposures. The corporate unit oversees the company’s clean energy and other investments. The firm differentiates itself by providing end-to-end risk solutions across insurance placement, claims handling, risk counseling, and investment activities, rather than focusing on a single service. Its goal is to help clients manage risk effectively, protect assets, and pursue value through diverse investments, including clean energy.

Company size: 10,001+
Company stage: IPO
Headquarters: Itasca, IL
Founded: 1927

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