Senior Manager – Data Product Delivery & Analytics

Carpenter Technology Corporation•Raleigh, NC

12d

About The Position

Carpenter Technology Corporation is seeking a highly skilled and forward-thinking data and analytics leader to drive the design, modernization, and strategic evolution of our enterprise data and analytics ecosystem. This role blends hands-on technical expertise with strategic leadership to shape how data is governed, delivered, visualized, and leveraged across the organization. The ideal candidate will lead the transformation of certified data products into scalable, trusted, and consumable analytics solutions using modern delivery tools, while influencing enterprise data strategy, architecture standards, and cross-functional adoption. This leader will also play a key role in advancing next-generation data platforms and AI-enabled analytics capabilities. This is a unique opportunity for a transformative leader who thrives on solving complex challenges and enabling enterprise intelligence through data.

Requirements

Bachelor’s degree in Data Analytics, Computer Science, Information Systems, Engineering, or related field (Master’s preferred)
10+ years of progressive experience in data, analytics, or business intelligence roles
Demonstrated experience leading enterprise analytics delivery and data product initiatives
Strong expertise in modern analytics platforms (e.g., Power BI, Tableau, ThoughtSpot)
Experience with enterprise data architectures, including medallion/lakehouse models
Proven experience developing semantic layers, KPI frameworks, and metric standardization
Strong understanding of data governance, data quality, and lineage practices
Experience enabling or working with AI/ML and advanced analytics solutions
Excellent stakeholder management and cross-functional leadership skills

Responsibilities

Lead the delivery of analytics solutions (dashboards, reports, semantic models) built on certified data products
Ensure outputs are scalable, reusable, and aligned with defined business outcomes
Drive the transition from fragmented reporting to product-based analytics consumption
Deliver consistent, high-quality analytics to support operational and executive decision-making
Lead development of analytics solutions using modern visualization and delivery tools (e.g., Power BI, ThoughtSpot, Tableau)
Ensure tools are utilized as a consumption layer and not used to redefine business logic
Optimize user experience, performance, scalability, and adoption of analytics solutions
Lead modernization and rationalization of legacy reporting environments
Contribute to the evolution of enterprise data strategy and analytics architecture
Influence how data products are structured, modeled, and consumed across domains
Define and enforce standards for semantic layers, datasets, and consumption patterns
Provide feedback to data engineering and platform teams to improve usability, performance, and reusability
Ensure alignment between business priorities, analytics delivery, and platform capabilities
Lead development of enterprise semantic models (metrics, dimensions, KPI frameworks)
Ensure consistent KPI definitions across all analytics outputs
Centralize metric logic to eliminate duplication and conflicting interpretations
Partner with business and data teams to align definitions and business meaning
Ensure analytics outputs are built on curated, trusted (Gold-layer) data products
Collaborate with data engineering teams to enforce medallion architecture (Bronze/Silver/Gold)
Validate data readiness, quality, and completeness prior to business consumption
Promote reuse of standardized datasets and models across use cases
Enforce use of certified, governed data products across all analytics delivery
Ensure compliance with enterprise standards for data quality, lineage, and usability
Strengthen trust by eliminating inconsistencies, duplication, and shadow reporting
Identify and drive opportunities to embed AI, automation, and decision support into analytics workflows
Enable use of data products for advanced analytics, machine learning, and AI use cases
Partner with AI/ML teams to ensure model-ready data availability and feature consistency
Advance adoption of intelligent analytics and automation
Serve as a strategic partner to business leaders, influencing data-driven decision-making
Collaborate across data product teams, engineering, platform teams, and advanced analytics teams
Drive adoption of enterprise analytics standards, tools, and best practices
Mentor and guide analysts, engineers, and product teams