Data Engineering Manager Senior

Gallagher•Rolling Meadows, IL

18d

About The Position

The Data Engineering Manager owns the design, delivery, and operation of Gallagher’s data platform for analytics, reporting, and AI. This builder-manager role leads and mentors data engineers, aligns engineering work with business goals, shapes the platform roadmap, negotiates priorities, reviews designs and code, unblocks engineers, and oversees deliverables and operational support across teams, systems, and products. The role directs data and integration strategy, engineering standards, team development, stakeholder coordination, and the health of production data systems. It builds and maintains cloud-native ETL/ELT pipelines and enterprise integrations, establishes data modeling and semantic-layer standards, and improves data quality, observability, lineage, cataloging, scalability, and cost efficiency. The manager also develops data foundations for AI and advanced analytics, evaluates AI platform capabilities, establishes safeguards for sensitive enterprise data, and promotes responsible AI use within engineering.

Requirements

Relevant technical bachelor’s degree in Information Technology.
Proven experience leading cross-functional teams, managing business users, and aligning technical and business objectives.
At least 5 years of experience leading a technical team.
At least 7 years of data engineering experience using technologies such as Snowflake, Azure Data Factory, ADLS Gen 2, Azure Functions, Databricks, Synapse, and SQL Server.
Demonstrated experience delivering data foundations for AI or machine-learning workloads, such as feature pipelines, training datasets, embeddings and vector search, model deployment support, or LLM-based enterprise-data applications.
Practical understanding of governing AI use of sensitive data, including access control, masking, lineage, and auditability.
Understanding of the advantages, disadvantages, and best practices of implementing a data lake using Microsoft Azure Data Lake Storage.
Experience structuring a data lake for reliability, security, and performance.
Experience implementing ETL for data warehouse and business intelligence solutions.
Ability to write and read effective, modular, dynamic, parameterized, robust code and to establish and follow code standards and ETL frameworks.
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 business questions and identify improvement opportunities.
Experience working within an agile team.
Exceptional communication and interpersonal skills, including the ability to influence and guide stakeholders at all levels.
Demonstrated ability to manage business-user relationships, address concerns, and meet needs through effective communication and collaboration.
Experience navigating ambiguity and uncertainty in projects and delivering successful outcomes.
Proven experience managing deliverables across multiple teams and providing ongoing support for data systems and integrations.

Responsibilities

Lead requirements gathering, scope definition, and technical design for data and integration workflows, providing strategic and architectural direction.
Set and enforce engineering standards for code review, ETL/ELT frameworks, testing, documentation, and definition of done.
Identify risks in data initiatives and develop mitigation strategies.
Own production data-system and integration health, including SLAs, monitoring and alerting, incident response, and blameless post-incident reviews.
Contribute to platform and tooling selection, capacity planning, vendor evaluation, and budget forecasting, including cloud and platform consumption costs.
Hire, onboard, coach, and retain data engineers across onsite and offshore locations; manage performance, career development, and succession planning.
Build a scalable team operating model with clear ownership, sustainable on-call and support rotations, and fewer single points of failure.
Coordinate development and support teams across time zones and foster effective communication and collaboration.
Partner with data science and analytics leaders to align engineering capacity with modeling, reporting, and AI priorities.
Liaise between technical teams and business users to ensure solutions meet business needs and address concerns.
Manage deliverables across teams and products, ensuring timely completion and alignment with business priorities.
Communicate platform health, delivery status, and roadmap changes to technical and non-technical audiences.
Build and maintain cloud-native ETL/ELT infrastructure to ingest data from diverse sources, including with Azure Data Factory, Databricks, and Snowflake.
Construct and maintain enterprise integrations across Snowflake, Azure Synapse, Azure SQL, and SQL Server, including plans to retire overlapping or legacy platforms.
Own data modeling standards and the semantic layer used by analytics and BI tools.
Establish data quality, observability, lineage, and cataloging practices so consumers can assess dataset trustworthiness.
Design and implement process and infrastructure improvements to automate manual work, improve data delivery, increase scalability, and reduce cost.
Build data tools and curated datasets for analytics and data science teams, including foundations for BI reporting.
Reduce recurring support burden through automation, improved instrumentation, and permanent fixes.
Design and operate AI data foundations, including feature pipelines, training and evaluation datasets, and reproducible, point-in-time-correct modeling data.
Evaluate and integrate platform AI capabilities, such as in-warehouse LLM functions, managed model endpoints, and agent orchestration frameworks, and make build-versus-buy recommendations.
Establish AI data-use guardrails, including PII handling and masking, access controls, prompt and response logging, output evaluation, and auditability aligned with governance and regulatory requirements.
Promote effective and responsible use of AI in engineering, including coding assistants, automated testing and documentation, and pipeline generation, while measuring delivery and quality impact.