Senior Analytics Engineer

American Industrial Transport•Saint Charles, MO

6d•$120,000 - $130,000

About The Position

The Senior Analytics Engineer is responsible for designing, building, and maintaining the organization's enterprise analytics solutions by transforming enterprise data into trusted, scalable, and actionable business information. Beginning with data available in the bronze layer of the enterprise data platform, this role owns the lifecycle of data curation, business transformations, enterprise data modeling, semantic modeling, and analytics that enable informed decision-making across the organization. Working closely with business and technology leaders, the Senior Analytics Engineer develops reusable data products that improve operational performance, simplify enterprise reporting, and support data-driven decision making. In addition to delivering traditional analytics solutions, this role develops AI-ready data products and designs, builds, and implements enterprise AI capabilities including data agents, intelligent search, AI-assisted decision making, and future intelligent business solutions. This position represents the evolution of traditional reporting roles into a modern analytics engineering discipline. By combining data curation, enterprise modeling, semantic design, analytics engineering, and data visualization into a single role, the Senior Analytics Engineer delivers governed, reusable, and scalable data products that reduce complexity, standardize business metrics, and accelerate the organization's ability to leverage analytics and AI.

Requirements

Bachelor's degree in Computer Science, Information Systems, Data Analytics, Engineering, or related discipline (or equivalent experience).
7+ years of experience in data engineering, analytics engineering, business intelligence, or related technical roles.
Strong SQL development experience.
Experience designing dimensional and semantic data models.
Experience developing modern dashboards and analytical applications.
Strong understanding of data governance, data quality, and metadata management.
Excellent communication, collaboration, and business partnership skills.
Experience with Microsoft Fabric, Azure Data Factory, Azure SQL, Microsoft Power BI, and related Microsoft data technologies.
Experience with medallion data architectures.
Knowledge of APIs, cloud integration, and event-driven data architectures.
Understanding of emerging agentic AI solutions.
Experience supporting AI, machine learning, or advanced analytics initiatives.
Familiarity with Microsoft Purview or other metadata and governance platforms.
Experience working with ERP platforms such as SAP.

Nice To Haves

Experience in regulated or asset-intensive industries (e.g., rail, manufacturing, logistics) preferred.

Responsibilities

Design, build, and maintain curated silver and gold data products following the organization's medallion architecture.
Develop scalable enterprise data models, dimensional models, and semantic models supporting operational, financial, commercial, and executive analytics.
Create reusable business metrics and standardized calculations that provide consistent reporting across the organization.
Maintain and enhance enterprise data products and semantic models to meet evolving business needs.
Develop scalable dashboards, scorecards, executive reporting, and self-service analytics solutions.
Translate business requirements into intuitive analytical solutions that support decision-making.
Design reporting solutions that simplify the reporting environment, reduce redundancy, and improve usability.
Continuously evaluate and rationalize enterprise reporting assets by retiring redundant reports, standardizing enterprise reporting, and promoting the use of governed semantic models.
Improve reporting performance, adoption, and overall user experience.
Work directly with business leaders to understand operational challenges and identify opportunities to improve decision-making through data.
Recommend solutions that simplify business processes and deliver measurable business value.
Help establish enterprise KPIs and performance measures aligned with organizational objectives.
Own analytics initiatives from requirements gathering through solution design, development, implementation, and ongoing enhancement.
Partner with the Data Governance team to improve data quality, metadata, and lineage across enterprise data assets.
Implement validation, reconciliation, and monitoring processes to ensure trusted and reliable analytical outcomes.
Understand and document the end-to-end data lifecycle from source systems through enterprise data products, semantic models, and business reporting.
Develop AI-ready data products, semantic models, and curated data assets that support enterprise AI capabilities, including data agents, intelligent search, AI-assisted decision making, and future intelligent business solutions.
Design, develop, and implement AI solutions including data agents, intelligent search, AI-assisted analytics, and other agentic capabilities.
Partner with Data Science and business stakeholders to enable advanced analytics initiatives through trusted enterprise data assets.
Support enterprise data platform modernization and continuous improvement initiatives.