Senior Manager of Data and Analytics

GPRS•Dallas, TX

8d•Remote

About The Position

The Senior Manager of Data and Analytics is responsible for leading the organization's data strategy, analytics, engineering, business intelligence, and AI-enablement initiatives. This role serves as the bridge between business leaders and technical resources, translating complex business questions into actionable insights, scalable data solutions, and measurable operational improvements. This leader will manage and prioritize the work of analysts, analytics engineers, data engineers, and future AI-focused resources while remaining deeply involved in technical execution. The ideal candidate is equally comfortable building complex analytical models, designing data architecture, performing advanced business analysis, presenting findings to executives, and identifying new opportunities where data can improve operational efficiency, customer experience, revenue generation, profitability, and organizational effectiveness. The successful candidate combines strategic thinking with intellectual curiosity and strong technical depth. They proactively seek out opportunities for improvement, challenge assumptions through analysis, and build scalable data capabilities that enable data-driven decision making throughout the organization.

Requirements

Bachelor's degree in Data Analytics, Computer Science, Information Systems, Engineering, Mathematics, Statistics, Economics, or a related field.
7+ years of experience in analytics, business intelligence, data engineering, data science, or related disciplines.
3+ years of experience leading teams, projects, or enterprise initiatives.
Advanced proficiency in SQL and data analysis.
Strong experience with Python or similar analytical programming languages.
Experience designing and supporting data warehouses, lakehouses, and enterprise reporting environments.
Experience developing executive dashboards and KPI frameworks.
Strong understanding of ETL/ELT processes, data modeling, and data governance principles.
Demonstrated ability to communicate complex analytical concepts to both technical and non-technical audiences.
Proven ability to drive projects from problem definition through implementation and measurement.

Nice To Haves

Master's degree in Data Analytics, Data Science, Computer Science, Business Analytics, or a related field.
Experience with Databricks, DBT, Azure, Snowflake, Fabric, or similar modern data platforms.
Experience with semantic modeling and self-service analytics environments.
Experience supporting AI, machine learning, or large language model initiatives.
Experience within high-growth or multi-location organizations.

Responsibilities

Lead, mentor, and develop data analysts, data engineers, and AI engineers.
Define quarterly and annual roadmaps for data, analytics, and AI initiatives.
Prioritize team initiatives and align resources against organizational objectives.
Balance tactical reporting requests with long-term strategic initiatives.
Establish standards for analytics development, data governance, documentation, testing, and deployment.
Drive accountability for project execution, delivery timelines, and business outcomes.
Partner directly with executive leadership and business unit leaders to solve complex business problems.
Translate ambiguous business questions into analytical investigations and actionable recommendations.
Perform deep quantitative and qualitative analysis to identify operational, financial, customer, and market opportunities.
Present findings and recommendations to senior leadership in a clear and concise manner.
Evaluate organizational performance and recommend solutions to improve efficiency, effectiveness, and profitability.
Proactively identify areas for analysis without waiting for formal requests.
Develop frameworks and methodologies for measuring business performance.
Own organizational KPI definitions, governance, and reporting standards.
Build and maintain executive dashboards and operational reporting solutions.
Conduct advanced analyses related to productivity, labor utilization, scheduling optimization, sales performance, customer experience, revenue growth, margin improvement, and operational efficiency.
Develop forecasting, trend analysis, and predictive analytics capabilities.
Provide ongoing performance monitoring and actionable recommendations based on data.
Guide development of enterprise data warehouse and lakehouse environments.
Oversee data pipeline design, ETL/ELT processes, data integration, and data quality initiatives.
Support master data management and enterprise data governance efforts.
Design scalable data models that support reporting, analytics, AI, and future growth.
Establish standards for data architecture, quality, documentation, lineage, and stewardship.
Ensure reliability, accuracy, accessibility, and scalability of enterprise data assets.
Partner with business and technology leaders to identify and prioritize AI opportunities.
Lead initiatives that enable conversational analytics, intelligent agents, automation, and AI-driven decision support.
Support development of semantic models, business metadata layers, and governed datasets that improve AI effectiveness.
Evaluate emerging technologies and recommend innovative approaches to improve organizational performance.
Help define the long-term roadmap for enterprise analytics, data science, and AI capabilities.