Senior Manager - Data Product Delivery & Analytics

Carpenter Technology Corporation Senior Manager, Data & Analytics (Enterprise Data Products)

We are 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.

Data Product Delivery & Analytics Execution

• 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

Analytics Delivery Tools & Consumption Layer

• Lead development of analytics solutions using modern visualization and delivery tools (e.g., Power BI, Thought Spot, 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

Enterprise Data Strategy & Architecture Influence

• 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

Semantic Layer & KPI Standardization

• 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

Medallion Architecture Alignment & Data Product Consumption

• 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

Data Governance, Trust & Quality

• 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

AI & Automation Enablement

• 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

Cross‑Functional Leadership & Influence

• 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

• Bachelor's degree in Data Analytics, Computer Science, Information Systems, Engineering, or related field…