Manager, Data Solutions II - Denver

Wilbur-Ellis•Denver, CO

1d•Onsite

About The Position

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.

Requirements

5+ years of experience in a developer or data engineering role, with strong exposure to the software development lifecycle (SDLC) and data platform delivery
5+ years of experience leading and developing technical teams, with a background in cloud data platform design, development, and implementation
7+ 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 orchestration tools (e.g., Fivetran, Coalesce, dbt, Airflow or equivalents)
Experience with Agile delivery methodologies
Bachelor’s degree in a related discipline or equivalent experience

Nice To Haves

Demonstrated people leadership with a strong commitment to coaching, mentoring, and performance development
Ability to manage multiple initiatives and priorities in a fast-paced, cross-functional environment
Clear and effective communication, with the ability to articulate technical concepts to business and executive audiences
Strong collaboration skills and ability to influence cross-functional teams without direct authority
Hands-on technical curiosity and a continuous improvement mindset
Highly motivated, organized, detail-oriented, and outcome-focused
Effective communication through written documentation, presentations, and executive updates

Responsibilities

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 DevOps 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.