Data & AI Engineer

Teichert•Sacramento, CA

1d•Onsite

About The Position

This position is responsible for designing, developing, and maintaining the data pipelines, infrastructure, and systems that enable reliable data integration, transformation, and delivery across Teichert’s enterprise data platform. The Data & AI Engineer partners with data scientists, analysts, and AI engineers to ensure data is high-quality, well-governed, and ready to support analytics, AI, and business intelligence, and applies foundational AI and agent-building skills to deliver solutions to business problems. This role sits within the Data & Technology Solutions department and contributes to the build-out of Teichert’s enterprise data platform and AI capabilities.

Requirements

Bachelor’s degree in Computer Science, Data Science, Software Engineering, Information Systems, or a related field.
Minimum 5 years of experience in data engineering, data management, or a related field, with demonstrated expertise in data integration, pipeline development, data quality, and generative AI and agentic AI use.
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.
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.

Nice To Haves

Experience in asset-intensive industries such as construction, mining, energy, transportation, or manufacturing is preferred.
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.

Responsibilities

Designs and develops data pipelines that extract data from various sources, transforms it into the desired format, and loads it into Teichert’s data platform.
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.
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.
Serves as a visible ambassador for data and AI across the organization.
Implements data quality checks and validations within pipelines to ensure accuracy, consistency, and completeness; partners with DAAI leadership on data governance practices.
Optimizes data pipelines and processing workflows for performance, scalability, and efficiency; monitors and tunes data systems and identifies and resolves performance bottlenecks.
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.