Senior Manager, Analytics Engineering; Charlotte​/Remote

Atlanta, Georgia, Austin AFP Main, Dallas, Texas, Raleigh, North Carolina, SEE Headquarters - Charlotte

Requisition

Job Description

The Senior Manager, Analytics Engineering leads a centralized analytics engineering function responsible for delivering scalable, business-ready data solutions across multiple commercial domains. This role operates within a hybrid model leveraging enterprise data infrastructure and Microsoft Fabric. As a hands‑on leader, this role is accountable for owning analytics data architecture and standards, managing delivery across a shared service model, leading a distributed team of Analytics Engineers, and personally contributing to complex data and analytics solutions.

Location:

Charlotte, NC (remote candidates also considered)

• Own the design and evolution of data pipelines, transformations, and data models
• Define standards for scalable and reusable datasets
• Ensure business logic is consistently structured and reusable across reporting and analytics
• Establish a clear and maintainable “last‑mile” data layer for business consumption
• Own and manage a centralized analytics engineering backlog across multiple domains
• Make prioritization and tradeoff decisions across competing business needs, balancing speed, scalability, and impact
• Establish clear intake, prioritization, and delivery processes
• Ensure consistent, high‑quality, and timely execution
• Operate a global shared analytics engineering function supporting multiple business domains simultaneously
• Allocate resources effectively across competing priorities
• Act as the primary escalation point for prioritization and delivery tradeoffs
• Balance speed, scalability, and data governance in all decisions
• Oversee the conversion of proof‑of‑concepts into scalable, production‑ready data solutions
• Enable fast iteration while progressively improving structure, performance, and reusability
• Standardize and scale business logic across datasets and reporting assets
• Personally contribute to complex data transformations, pipelines, and models
• Support debugging, optimization, and resolution of critical technical challenges
• Step in to accelerate delivery for high‑priority or high‑complexity work
• Lead and develop a team of Analytics Engineers operating in a distributed delivery model
• Set priorities, manage capacity, and drive accountability for delivery outcomes
• Coach team members on technical execution, data design, and delivery best practices
• Build a disciplined, high‑performing execution engine
• Partner with IT to align with enterprise data architecture, standards, and governance
• Operate within enterprise guardrails while maintaining flexibility and speed
• Collaborate with Insights Managers to translate business needs into scalable data solutions
• Determine when solutions should remain business‑owned vs. transition to IT for enterprise scale
• Ensure accuracy, consistency, and reliability of all data assets
• Establish validation, monitoring, and quality assurance processes
• Implement Data Ops best practices including version control, structured deployment, and reusable design
• Maintain clear documentation across data pipelines, datasets, and processes

• Bachelor’s degree in computer science, engineering, business, finance, or related field
• 8‑12+ years of experience in analytics engineering, data engineering, or business intelligence
• Proven experience leading teams in a global or distributed delivery model
• Deep expertise in SQL, ETL/ELT design, and data modeling
• Strong hands‑on experience with Microsoft Fabric (Lakehouse, notebooks, pipelines, semantic models) or similar modern data platforms
• Demonstrated ability to work with ambiguous, incomplete, or evolving data and business requirements
• Proven ability to manage multiple stakeholders, priorities, and competing deadlines
• Strong communication skills, translating technical concepts into business context
• Experience supporting commercial, sales, or operational analytics
• Experience working in hybrid IT + business‑owned data environments

• High‑quality, scalable data solutions delivered consistently across multiple domains
• Strong prioritization and effective backlog…