Accounting and Administrative Specialist

Five Rivers Cattle Feeding•Johnstown, CO

2d•Hybrid

About The Position

Responsible for the technical leadership, delivery oversight, prioritization, and operational maturity of the company’s data warehouse, pipelines, semantic models/cubes, and data experience layers. This role involves leading, coaching, and developing the Data & Analytics team, establishing team standards, and serving as a technical backstop. The Director will own the delivery and support of enterprise data and analytics solutions, including data warehouse architecture, data integration, semantic models, and enterprise reporting. They will guide solution design, oversee analytics initiatives, and maintain a roadmap for data and analytics capabilities. The role also requires providing technical oversight in data warehousing, ETL/ELT, dimensional modeling, and Power BI, while partnering closely with Software Development and IT leadership on data standards. Business engagement involves translating business goals into solutions, clarifying needs, and managing expectations. Project management discipline is crucial for analytics initiatives, including planning, scope definition, and quality assurance. The position also supports advanced analytics, AI, and enablement, assisting power users and defining governance for analytical and AI capabilities.

Requirements

Bachelor’s degree in Computer Science, Data Analytics, or a related technical field, or equivalent combination of education and experience.
Strong (5+ years) experience in data warehousing, analytics, business intelligence, and related IT disciplines.
Strong (5+ years) experience managing technical teams, leads, and major cross-functional initiatives.
Demonstrated experience delivering enterprise data and analytics solutions in a complex environment.
Strong working knowledge of: data warehousing concepts and platforms, data integration, ETL/ELT, and pipeline orchestration, dimensional modeling and semantic modeling, cubes and analytical structures, Dataset design, report development, and performance optimization.
Ability to review technical designs, guide architecture decisions, and coach developers on best practices.
Strong understanding of data quality, data governance, security, and supportability considerations.
Experience working with operational system data and partnering with software development teams.
Strong project management skills, including planning, prioritization, coordination, and delivery oversight.
Strong business analysis skills, including requirements elicitation, process understanding, and solution definition.
Strong QA mindset with experience in validation, testing, and release quality.
Excellent verbal and written communication skills with the ability to work effectively with both technical and business audiences.
Ability to build trust, influence decisions, and navigate dependencies across functions.
Strong problem-solving ability, sound judgment, and attention to detail.

Nice To Haves

Experience with Microsoft Foundry, OneLake, Azure ML Studio and similar machine learning platforms.
Experience supporting AI-enabled solutions, self-service analytics, or enterprise data enablement.
Experience with large-scale, industrial, operations-heavy, multi-site production animal agricultural.
Familiarity with change management and user enablement for analytics adoption.

Responsibilities

Lead, coach, and develop the Data & Analytics team, including developers and related technical resources.
Provide clear direction, work planning, prioritization, and performance management for the team.
Establish team standards, operating rhythms, delivery practices, and documentation expectations.
Serve as escalation point and technical backstop for warehouse, pipeline, model/cube, and reporting work.
Build cross-training and redundancy so function is sustainable and not dependent on any one individual.
Own the delivery and support of enterprise data and analytics solutions, including: data warehouse architecture and administration, data integration and pipelines, semantic models and cubes, enterprise reporting and dashboards.
Guide solution design to ensure data products are scalable, secure, maintainable, and business-aligned.
Oversee the intake, assessment, prioritization, and execution of analytics initiatives.
Maintain a roadmap for data and analytics capabilities that supports operational, financial, risk, HR, environmental, and executive reporting needs.
Ensure analytics delivery is treated as an enterprise capability serving the whole business, rather than a narrow sub-function of IT or any single department.
Provide technical oversight and practical guidance in enterprise data warehousing, ETL/ELT and data pipeline design, dimensional modeling and semantic layer design, cubes and analytical data structures, Power BI datasets, reports, dashboards, governance, and performance tuning.
Review architecture and design decisions to promote reliability, usability, scalability, and supportability.
Partner closely with Software Development to define, obtain, and improve operational data sources needed for analytics solutions.
Work with IT leadership to establish standards for data quality, metadata, naming, lineage, security, and lifecycle management.
Support troubleshooting and continuity of delivery by being capable of guiding, reviewing, and backing up the team’s technical work when needed.
Translate business goals into practical data and analytics solutions.
Partner with stakeholders across the company to clarify needs, define requirements, and manage expectations.
Serve as a primary liaison between technical teams and business partners for enterprise reporting and analytics initiatives.
Help stakeholders understand priorities, tradeoffs, dependencies, and delivery timelines.
Apply strong project management discipline to analytics initiatives, including planning, scope definition, milestone tracking, issue management, and status communication.
Perform or oversee business analysis activities such as requirements gathering, process review, use-case development, and solution validation.
Establish and enforce QA practices for data and analytics solutions, including testing strategy, reconciliation, validation, user acceptance support, and release readiness.
Promote high-quality outputs by ensuring reports, models, and pipelines are accurate, understandable, and fit for business use.
Support the company’s use of Microsoft Fabric, Foundry, Azure ML Studio, and advanced analytics tools.
Assist power users in applying machine learning, AI, and automation in practical business scenarios.
Enable and support technical power users across the company in their effective and responsible use of analytics, AI, and self-service tools.
Help define appropriate governance, support boundaries, and best practices for business-facing analytical and AI capabilities.