Manager, Data Solutions II - Denver

Investing in You, join a 100-year-old, family-owned leader in Ag – and grow with us!

Wilbur-Ellis invests heavily in our employees by offering skill development and training, competitive compensation and benefits, and a tradition of promoting from within for a broad range of career opportunities. And we foster a supportive, people-first work environment.“We know the confidence gap can get in the way of meeting spectacular candidates, so please don’t hesitate to apply — we’d love to hear from you.

”
Role Overview :

The Manager, Data & AI Solutions is responsible for leading the design, development, and operation of enterprise data platforms and AI-enabled solutions in a cloud environment. This role oversees the Data Solutions team, setting standards for modern data engineering, analytics engineering, and applied AI/ML best practices. The position requires a strong understanding of data movement, security, modeling, cloud-native architectures, and the responsible use of AI.This

role partners closely with business, product, analytics, and IT leaders to identify, define, and deliver scalable data and AI solutions that drive measurable business outcomes. The Manager leads a diverse team throughout the full solution lifecycle—from requirements and architecture through production deployment and support—with emphasis on data ingestion, data architecture, analytics enablement, and the integration of AI/ML and automation capabilities.

Hands-on technical leadership is expected as needed to ensure quality, performance, and timely delivery.

A Sample of What You’ll Do in This Role:

Lead the data and AI solutions team of 5–10 people with a clear strategic vision aligned to enterprise data, analytics, and AI roadmaps.

Partner with business and functional leaders to identify high-value data, analytics, and AI opportunities.

Oversee the development and operation of cloud-based data ingestion, data lake, and data warehouse solutions.

Participate in the AI Center of Excellence (CoE) to define standards, patterns, and governance for data engineering, analytics, and AI solutions.

Guide the team across the full lifecycle from requirements and architecture through development, testing, deployment, and support.

Provide technical leadership for Snowflake-based data platforms and associated analytics layers.

Lead CI/CD practices for data and analytics engineering using Azure Dev Ops and Agile methodologies; manage sprint planning and execution.

Drive adoption of AI-enabled capabilities, including advanced analytics, machine learning models, and generative AI solutions where appropriate.

Ensure data and AI solutions meet security, privacy, governance, and compliance standards.

Monitor emerging data, analytics, and AI trends and evaluate their applicability to the organization.

Build and maintain strong relationships with business leaders, stakeholders, and cross-functional technology teams.

What You Bring to the Role:5+ years of experience in a developer or data engineering role, with strong exposure to the software development lifecycle (SDLC) and data platform delivery5+ years of experience leading and developing technical teams, with a background in cloud data platform design, development, and implementation7+ years of experience with DW/BI and cloud data technologies (Snowflake strongly preferred)
Strong expertise in data modeling, including conceptual, logical, and dimensional (star/snowflake) schemas

Advanced SQL skills for analyzing and interpreting enterprise-scale datasets

Deep understanding of relational databases, data warehousing patterns, and source system integration

Understanding of generative AI use cases for analytics, data productivity, and business workflows (e.g., copilots, natural language querying, summarization)
Experience delivering AI/ML-enabled data products, such as predictive models, forecasting, recommendations, anomaly detection, or intelligent automation

Experience supporting self-service analytics, semantic layers, and data consumption tools

Strong understanding of data governance concepts including data quality, metadata management, data catalogs, and master data management

Working knowledge of ELT/ETL and…