Data, Analytics & AI Engineer ONI

O'Neal Industries•Birmingham, AL

About The Position

This role is responsible for designing, building, and maintaining data pipelines, data models, and AI/ML solutions to support analytics, reporting, and automation use cases. The engineer will work with various systems and technologies, including Boomi, Azure Data Factory, Azure SQL, Power BI, and Microsoft Fabric. The position involves collaborating with stakeholders, IT teams, and other business units to deliver actionable insights and drive data-driven decision-making. A key aspect of the role is to identify, prototype, and operationalize AI and machine learning solutions, including agentic workflows, and to act as an internal advocate for responsible AI adoption.

Requirements

Bachelor’s degree or higher in Computer Science, Data Science, Statistics, Applied Mathematics, Engineering, or a related field.
Strong experience building and supporting data pipelines and system integrations, preferably with Azure Data Factory and/or Boomi.
Proficiency in SQL and relational data modeling; experience with Azure SQL Database or similar platforms.
Hands‑on experience developing and maintaining analytics and semantic models using Power BI.
Programming experience in one or more languages such as Python (including notebooks), with the ability to apply them to data engineering, analytics, and AI use cases.
Experience applying AI/ML techniques to real‑world business problems, including model development, evaluation, and operationalization.
Strong analytical thinking, problem‑solving, and attention to detail.
Ability to communicate complex technical concepts clearly to both technical and non‑technical audiences.
Self‑driven, curious, and comfortable working in a dynamic, evolving technical environment.

Nice To Haves

Experience or strong interest in Microsoft Fabric, including lake house, warehouse, notebooks, real time analytics, and AI workloads.
Familiarity with agentic AI patterns, workflow orchestration, and automation platforms.
Experience supporting enterprise data platforms in regulated or security conscious environments.
Understanding of modern data architecture concepts (ELT, semantic layers, governance, lineage).
Experience working within large, distributed organizations or multi affiliate environments.
Strong customer focused mindset with the ability to manage expectations and build trusted partnerships.

Responsibilities

Design, build, and maintain reliable, scalable data pipelines using Boomi and Azure Data Factory to integrate data from ERP, EHS, operational, and corporate systems.
Develop and manage curated data models in Azure SQL and future MS Fabric environment lake house or warehouse architectures to support analytics, AI, and reporting use cases.
Ensure data quality, consistency, and performance across pipelines, models, and downstream consumers.
Contribute to data architecture standards, patterns, and best practices as ONI evolves toward Microsoft Fabric.
Develop and maintain Power BI semantic models, dashboards, and reports that deliver actionable insights to executives and business teams.
Partner with stakeholders to translate business questions into metrics, KPIs, and analytical solutions.
Promote self‑service analytics through well‑designed datasets, documentation, and governance.
Identify, prototype, and deliver AI and machine learning solutions that improve decision‑making, forecasting, anomaly detection, classification, and automation.
Design and implement agentic workflows that combine data, analytics, and AI models to automate multi‑step business processes, decision support, and operational actions.
Leverage Azure‑based AI services and emerging Fabric capabilities (e.g., notebooks, ML, real‑time intelligence) to operationalize AI solutions.
Act as an internal advocate and advisor on responsible AI adoption, helping business units understand practical and strategic AI opportunities.
Collaborate with IT, security, and DevOps teams to ensure solutions align with enterprise standards for security, compliance, and reliability.
Partner with affiliate companies and cross‑functional ONI teams as a resource on key initiatives.
Prioritize, scope, and manage data and AI initiatives with clear success metrics and business outcomes.
Explore, evaluate, and pilot new data, analytics, and AI technologies with a bias toward business value.
Contribute to a culture of data‑driven decision‑making, experimentation, and continuous improvement across ONI.