Senior Manager – AI, Data, and Analytics

Jobtailor; Illinois; On-site; USD 120,000 - 180,000; Full time

Job summary

Jobtailor in the United States is seeking a senior data analytics leader to shape and govern our data architecture and analytics roadmap. You will guide cross-functional teams to operationalize AI/GenAI initiatives and translate business priorities into measurable outcomes.

Responsibilities include establishing data governance, observability, and scalable data delivery practices while partnering with executives to realize value from analytics products and dashboards across functions.

Qualifications

• Minimum 7–10 years of experience in data analytics.
• Strong foundation in data architecture, analytics, and data visualization tools.
• Demonstrated experience defining and governing enterprise data architecture, including data integration, data quality, metadata, lineage, and data governance frameworks.
• Strong experience with modern data delivery practices such as DataOps (CI/CD, automated testing, monitoring/observability, and documentation) in support of regulated, auditable environments.
• Knowledge of AI, machine learning, and GenAI applications in business and financial analytics.
• Strong business acumen with the ability to translate complex analyses into actionable recommendations.
• Proven leadership skills, including experience influencing senior stakeholders and leading cross-functional teams.
• Bachelor’s degree in data science, Statistics, Computer Science, Business Administration, or a related field.

Responsibilities

• Define and implement enterprise data architecture and governance.
• Lead cross-functional teams to deliver data analytics initiatives.
• Operationalize AI/GenAI use cases from intake to scale.
• Partner with business leaders to translate priorities into measurable outcomes.
• Establish data observability, quality, and metadata management.
• Set roadmaps with clear ownership, stage gates, and value realization.

Skills

Data Analytics

Data Architecture

Data Visualization Tools

DataOps Practices

AI Applications in Business

Leadership & Influence

Education

Bachelor's degree in data science

Tools

Power BI

Tableau

Cloud Data Platforms

Vector Databases

Retrieval-Augmented Generation

Job description

• Serve as a first point of contact for Corporate Functions on AI, Data, and Analytics tooling and capabilities, advising on best-fit solutions (enterprise platforms vs. productivity-embedded solutions) based on business use cases and risk considerations.
• Help operationalize intake-to-value delivery for AI/GenAI use cases (intake → assess data readiness → pilot → scale), including clear stage gates, ownership, and benefits tracking in partnership with business, risk, and engineering stakeholders.
• Partner with business leaders and BRMs to shape demand and translate priorities into data domains, analytics use cases, and measurable business outcomes.
• Incorporate data and GenAI initiatives into prioritized roadmaps with clear outcomes, dependencies, and data readiness, ensuring alignment with organizational strategic priorities.
• Define and enable agentic AI (e.g., copilots, task-oriented agents, and workflow-embedded AI) by establishing the data, architecture, and governance foundations that allow AI agents to safely reason, act, and integrate with enterprise systems.
• Partner with business leaders and external vendors establishing a data and analytics strategy and operating model (intake, prioritization, delivery, adoption, and value realization) for each function, including governance models that ensure scalable, AI-ready solutions.
• Evaluate and guide adoption of established and emerging technologies (e.g., cloud data platforms, vector databases, retrieval-augmented generation (RAG), advanced analytics, and real-time inference architectures) to enhance corporate capabilities.
• Promote enterprise reporting standards and governed semantic layers to ensure consistent definitions of key metrics across Corporate Functions.
• Improve reliability and trust through data observability and operational controls (monitoring, alerting, SLAs, and automated checks) in collaboration with platform and integration teams.
• Identify risks and support mitigation strategies impacting financial and operational performance.
• Communicate complex data, architecture, and analytics concepts to non-technical stakeholders in a clear, actionable manner.
• Continuously evaluate and improve data and analytics processes and methodologies to increase reliability, usability, and time-to-insight.
• Allocate resources effectively to ensure successful execution of data and analytics initiatives.
• Track and report on the performance, adoption, and realized value of data and analytics products and initiatives (e.g., dashboards, regular stakeholder reviews, and post-implementation lessons learned.)

Requirements

• Minimum of 7–10 years of experience in data analytics.
• Strong technical foundation in data architecture and analytics, including experience with data visualization tools (e.g., Power BI, Tableau) and advanced analytics tooling (e.g., SQL, Python, R).
• Demonstrated experience defining and governing enterprise data architecture, including data integration, data quality, metadata, lineage, and data governance frameworks.
• Strong experience with modern data delivery practices such as DataOps (CI/CD, automated testing, monitoring/observability, and documentation) in support of regulated, auditable environments.
• Knowledge of AI, machine learning, and GenAI applications in business and financial analytics.
• Strong business acumen with the ability to translate complex analyses into actionable recommendations.
• Proven leadership skills, including experience influencing senior stakeholders and leading cross-functional teams.
• Bachelor’s degree in data science, Statistics, Computer Science, Business Administration, or a related field.

Core Competencies

Demonstrates expertise in data architecture, analytics, and AI applications, with a strong focus on operationalizing data initiatives and delivering measurable business outcomes. Proven ability to communicate complex concepts to diverse stakeholders and lead cross-functional teams in a data-driven environment.

Highest-signal resume keywords

• Data Architecture
• Data Visualization Tools
• DataOps Practices
• AI Applications in Business
• Leadership and Stakeholder Influence

ATS Optimization Keywords

Hard Skills

• Data Analytics
• SQL
• Python
• R
• Data Integration
• Data Quality
• Metadata Management
• Data Governance
• Advanced Analytics
• Data Delivery Practices

Soft Skills

• Business Acumen
• Communication Skills
• Cross-Functional Team Leadership

Industry Keywords

• Data Governance Frameworks
• Data Observability
• Operational Controls
• Enterprise Reporting Standards
• Risk Mitigation Strategies

Tools & Technologies

• Power BI
• Tableau
• Cloud Data Platforms
• Vector Databases
• Retrieval-Augmented Generation