Data & AI Engineer

Essential duties and responsibilities, i.e. those which are basic, necessary, and an integral part of the job, are indicated below:

1. Designs and develops data pipelines that extract data from various sources, transforms it into the desired format, and loads it into data platform.


2. Integrates data from databases, data warehouses, APIs, and external systems, including telematics, work order, and other operational systems; ensures data consistency and integrity during integration, performing validation and cleaning as needed; develops and maintains APIs as needed to enable data exchange between systems.


3. Applies foundational AI and agent-building skills, such as working with large language models, basic tool use, and retrieval-augmented generation, to support business use cases that require AI and data platform features.


4. Serves as a visible ambassador for data and AI across the organization.


5. Implements data quality checks and validations within pipelines to ensure accuracy, consistency, and completeness; partners with leadership on data governance practices.


6. Optimizes data pipelines and processing workflows for performance, scalability, and efficiency; monitors and tunes data systems and identifies and resolves performance bottlenecks.


7. Collaborates with data scientists and analysts to optimize models and algorithms for data quality, security, and governance; supports delivery and educates end users on data products.

Specific Job Requirements:

• Successful completion of pre-employment drug, alcohol, and background investigation.


• Ability to build trust and rapport quickly with employees at all levels, demystifying data and AI for non-technical audiences, and championing responsible adoption through day-to-day partnership.


• Demonstrated expertise in designing, building, and maintaining scalable data pipelines and ETL/ELT processes.


• Proficiency with cloud data platforms and services such as Azure and Databricks; experience with modern data warehousing and data architecture concepts.


• Strong programming skills in Python and SQL; working knowledge of additional languages (e.g., Java, R) is a plus.


• Hands-on experience applying AI and building foundational AI agents (e.g., using large language models, basic tool use, or retrieval-augmented generation) to real use cases required.


• Experience with MLOps/AIOps practices for deploying, monitoring, and maintaining models and data/AI pipelines in production preferred.


• Experience with version control tools (e.g., GitHub, Azure DevOps), Agile software development best practices, AI-assisted coding tools (e.g., Claude Code) to accelerate development; and data visualization platforms (e.g., Power BI, Tableau) preferred.


• Ability to collaborate across teams of varying technical backgrounds to support delivery.


• Strong problem-solving and debugging skills, including the ability to diagnose issues in unfamiliar code or systems.


• Ability to translate between technical, business, and executive stakeholders on data sources, data management concepts, and analytical approaches.


• Ability to preserve confidential and proprietary information and successfully avoid conflicts of interest.


• Must be able to prioritize, manage multiple workstreams, and recognize when to seek direction.