Manager Data Intelligence

Acima•Draper, UT

2d•Onsite

About The Position

We are seeking an experienced and technically strong Data, knowledge, and Intelligence Manager to lead the planning, design, development, and delivery of enterprise data intelligence products. This role acts as the bridge between business stakeholders, product managers, and engineering teams by translating business needs into scalable data solutions. The ideal candidate combines strong data architecture, technical leadership, experience in enterprise data products management, familiarity with Generative AI, LLMs, and agentic AI concepts (experience implementing AI use cases is preferred). And Agile delivery skills to ensure high-quality, dependable, and scalable data intelligence products leveraging approved enterprise tools, frameworks, and patterns.

Requirements

15 years in data, analytics, engineering, data architecture, data product management, or related roles.
5+ years leading enterprise data products or AI-enabled products.
Experience building AI, analytics, or data products/platforms at scale.
Demonstrated success delivering measurable business value from data and intelligence products.
Strong experience gathering and documenting business requirements.
Experience creating and managing Jira stories, epics, and Agile backlogs.
Strong understanding of data architecture, data modeling, and data integration patterns.
Experience reviewing code and enforcing engineering best practices.
Excellent communication, leadership, stakeholder management, and problem-solving skills.
Ability to balance strategic planning with hands-on technical execution.
Bachelor’s degree in computer science, Information Systems, Data Engineering, or a related field.
Knowledge of data warehouse, lakehouse, and data mesh architectures.
Familiarity with platforms such as Snowflake, Databricks, or similar technologies.
Business Value Realization
Stakeholder Management & Executive Communication
Data Strategy & Governance
Data Architecture & Data Modeling (Dimensional and Relational)
Experience implementing AI solutions.
Business Requirements Gathering
Agile/Scrum Methodologies
Jira Backlog Management
Enterprise Data Warehousing
Snowflake, Cloud Data Warehouses
Python
SQL
ETL/ELT Design and Development
Data Quality & Data Governance
Code Reviews & Quality Assurance
Production Support & Technical Troubleshooting
Performance Tuning & Optimization
Resource Planning & Capacity Management
CI/CD and DevOps Practices

Nice To Haves

Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), Knowledge Graphs, Vector Databases, Semantic Search, Prompt Engineering, AI Product Management

Responsibilities

Partner with business VPs/Directors/users, product managers, and leadership teams to understand business needs, objectives and develop use cases/requirements for enterprise data, Knowledge, and Intelligence products.
Facilitate requirement discovery workshops and stakeholder discussions.
Drive alignment between business priorities and data product roadmaps.
Evaluate and prioritize Generative AI, predictive analytics, machine learning, and intelligent automation use cases.
Function as the primary liaison between business and data/technical teams.
Translate roadmaps, business use cases/business requirements into actionable deliverables and guide creation of clear functional and technical specifications.
Deliver trusted data and intelligence products and self-service capabilities to business users.
Enable data-driven decision-making through dashboards, insights, and AI-powered capabilities & solutions.
Promote data literacy and AI adoption across the organization.
Define and provide architecture & Solution guidance for enterprise data, Knowledge, and Intelligence products.
Ensure solutions align with established data architecture standards, design patterns, and engineering frameworks.
Review solution designs to ensure scalability, performance, governance, security, and reusability.
Collaborate with architects and engineering teams to establish best practices and technical standards.
Develop and manage product backlogs, roadmaps, and release plans - Function as the defacto portfolio/product manager - create and manage Jira epics, features, user stories, tasks, and acceptance criteria based on business requirements.
Prioritize features based on business value, customer impact, and strategic alignment - Define product requirements, user stories, acceptance criteria, and success metrics.
Lead sprint planning, backlog grooming, sprint reviews, and retrospectives.
Ensure requirements are clearly documented and understood by development teams.
Monitor project progress and proactively manage risks, dependencies, and escalations.
Ensure data products comply with regulatory, privacy, security, and compliance requirements.
Drive metadata management, data lineage, cataloging, and discoverability initiatives.
Leverage AI where possible to implement the data governance capabilities and solutions.
Monitor and improve data reliability, accuracy, consistency, and trustworthiness.
Develop quarterly resource and capacity plans based on current project commitments, strategic priorities, and future demand.
Evaluate team utilization and forecast staffing requirements.
Partner with leadership to prioritize work and optimize resource allocation.
Support budget planning and workforce planning activities.
Conduct architecture, design, and code reviews to ensure adherence to standards and best practices.
Verify the quality, performance, reliability, and maintainability of data products.
Drive implementation of data quality, testing, monitoring, and operational excellence practices.
Establish and enforce coding standards, development frameworks, and governance processes.
Foster a culture of experimentation, innovation, and AI-driven problem solving.
Mentor teams on product thinking, data strategy, and AI best practices.
Stay current on industry trends, emerging technologies, and competitive developments.
Drive enterprise-wide AI and data product transformation initiatives.
Lead daily stand-up meetings and coordinate cross-functional team activities.
Provide mentorship and technical guidance to data engineers and developers.
Foster a culture of continuous improvement and knowledge sharing.
Facilitate collaboration across engineering, architecture, product management, and business teams.
Serve as the technical escalation point for critical production issues and complex data challenges.
Perform direct troubleshooting and root cause analysis when necessary.
Guide teams through issue resolution and implementation of preventative measures.
Support production deployments and operational readiness activities.