Senior Manager, Enterprise Data Platform

We are looking for a Senior Manager, Enterprise Data Platform to join our team.

This is a remote (within the US) role, reporting to the Senior Director, Enterprise Data Platform in the IT Data Strategy department.

In this role, you will be a key technical and strategic leader responsible for driving the architecture, strategy, and continuous evolution of our modern enterprise data platform.

You will lead the central data team, balancing its evolution into a highly effective, self-service platform team while actively supporting critical, high-impact, organization-wide data initiatives.

A core focus of this role is preparing both the central data team and federated business intelligence units (such as Sales, Marketing, and other internal business functions) to become AI-ready, implementing strategies to embed AI directly into daily data analytics.

This is a critical leadership position requiring a passionate innovator who is equally comfortable defining high-level strategy, establishing platform governance, and rolling up their sleeves to tackle complex, hands-on technical challenges.

What you’ll do (Role Expectations)

• Drive Platform Strategy, Architecture, & Evolution: Define the long-term technical roadmap, architecture, and strategy for the Enterprise Data Platform (utilizing Snowflake, dbt, Matillion DPC, and Streamlit). Balance the evolution toward modern platform operating models (medallion and mesh architectures) while actively supporting critical, high-impact, organization-wide business initiatives
• Enable Self-Service & Mesh Architectures: Empower decentralized business functions (such as Sales, Marketing, and Finance) to be entirely self-sufficient. Establish the fundamental analytics layers (Raw, Transform, Analytics) and deliver standard data contracts, allowing federated BI teams to easily build and run their own custom data models
• Embed AI into Data, Pipelines, & Analytics: Advance both the central and federated teams to become truly AI-ready. Design and implement strategies to embed AI directly into enterprise analytics, including building complex data pipelines (ETL), custom data apps, and Retrieval-Augmented Generation (RAG / Graph RAG) architectures directly on the data platform
• Architect AI-Driven Platform Operations & Governance: Implement frameworks for robust platform governance, data observability, and transparent cost attribution to optimize compute resources and Snowflake token consumption. You will be directly responsible for building and embedding automated AI systems (AIOps) to streamline platform operations—leveraging AI/LLMs for token cost optimization, automated metadata cataloging, and auto-triage of daily pipeline alerts
• Lead with Continuous Innovation & Hands-On Execution: Lead from the front as a hands-on manager who can jump into the codebase to tackle complex projects, prototype advanced workflows, or troubleshoot critical pipelines when required. Constantly research and evaluate the latest market trends and modern data technologies to keep our platform ecosystem at the cutting edge

• Act as an owner with a strong bias for action, navigating seamlessly between defining high-level strategy and executing hands-on technical tasks.
• Approach complex data challenges as a natural problem-solver, energized by finding elegant solutions that deliver maximum business impact.
• Lead with deep integrity and accountability, building trust across the organization through transparent actions and high standards.
• Think at scale by connecting daily engineering initiatives to the global company mission and designing robust, future-proof data solutions.
• Foster high-trust collaboration by championing team success, participating in a healthy feedback culture, and driving cross-functional alignment.

• AI Readiness Strategy: Demonstrated experience driving technical strategies to transition traditional data structures and business intelligence teams into AI-ready units
• Proven Leadership: 5+ years of experience leading, mentoring, and growing high-performing data engineering, platform engineering, or analytics engineering teams
• Snowflake & dbt Expertise: Deep architectural and operational knowledge of Snowflake and dbt (analytics engineering, modeling standards, testing)
• Enterprise AI Infrastructure & Data Prep: Hands-on knowledge of designing data pipelines (ETL), analytics, and building/serving Retrieval-Augmented Generation (RAG) architectures—including Graph RAG—on cloud data platforms
• Self-Service & Mesh Experience: Practical experience implementing or operating within a self-service data platform environment and a Data Mesh architectural framework (Raw, Transform, Analytics)
• Modern ETL & Engineering Foundations: Strong hands-on experience with cloud-native ETL/ELT tools (preferably Matillion Data Productivity Cloud or similar) and strong software engineering skills in Python and SQL (CI/CD, Git, automated testing)

What Will Make You Stand Out (Preferred Qualifications)

• Extreme Technical Depth: The ability to jump into the codebase, write complex code/models, and confidently lead the implementation of complex, highly technical data projects
• Advanced AI & Applications in Snowflake: Experience leveraging Snowflake’s advanced AI capabilities (e.g., Cortex, Snowpark) and building custom data applications using Streamlit

#LI-DY1 #LI-remote

#LI-DY1 #LI-remote

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