AI Data Engineer

Gallagher•Rolling Meadows, IL

About The Position

The AI Data Engineer develops, maintains, and tests data-generation infrastructure and builds and maintains data pipeline architectures to meet business needs. The role assembles complex datasets, improves internal processes by automating manual work, and optimizes data delivery and infrastructure scalability. It builds infrastructure for extracting, transforming, and loading data from varied sources using SQL and big-data technologies. The position also supports stakeholders with data-related technical issues and infrastructure needs, maintains secure separation of data across national boundaries, data centers, and AWS regions, and develops tools that support analytics and data science teams in building and improving products. This career-level role applies advanced knowledge, manages products, systems, projects, or processes with limited supervision, addresses moderately complex IT problems, and may coach and review the work of lower-level professionals.

Requirements

University degree and a minimum of 5 years of prior relevant experience, or equivalent experience.
Advanced knowledge of the relevant sub-function, typically gained through advanced education and work experience.

Nice To Haves

SQL and Python experience.
Azure Data Factory experience.
Databricks experience.
Spark experience; noted as a plus.
Data transformation experience.
Cloud data experience.
Distributed data warehouse knowledge.
Data lakes knowledge.
Communication and stakeholder management skills.

Responsibilities

Develop, maintain, and test infrastructure for data generation.
Create and maintain data pipeline architecture and assemble data to meet business needs.
Assemble large, complex datasets that meet functional and nonfunctional requirements.
Identify, design, and implement process improvements; automate manual processes and optimize data delivery and infrastructure scalability.
Build infrastructure for extracting, transforming, and loading data from varied sources using SQL and big-data technologies.
Work with executive, product, data, and design stakeholders on data-related technical issues and infrastructure needs.
Keep data separated and secure across national boundaries, data centers, and AWS regions.
Create data tools for analytics and data science team members to build and optimize products.
Oversee data-generation infrastructure.
Build analytics tools that provide actionable insights and support data science and analytics teams.
Manage products, systems, projects, or processes independently with limited supervision.
Address difficult, moderately complex IT-function problems.
Coach and review the work of lower-level professionals.