Data Governance Lead

Dynatrace•Boston, MA

6d•Hybrid

About The Position

Dynatrace's Data & Analytics organization is building a scalable, AI-ready, self-service data ecosystem and standing up a dedicated Data Governance function to define the standards and practices that make data trustworthy and safe for analytics and AI. We treat governance as an enabler, not a gate, a central hub setting companywide standards while partnering with decentralized data owners and stewards. This is a foundational hire: an experienced, strategic and collaborative Data Governance Lead to shape the companywide data governance framework. You will define the enterprise standards, processes and operating model for trusted, scalable data and establish the vision and framework for governance. You will partner with engineering to build the platform capabilities that make governance real with reliable data for both analytics and AI, operating within a hub-and-spoke model that sets central standards while decentralized owners, stewards and data product managers apply them. You will help establish AI-ready data and the model for how data and AI can be used responsibly, including the self-service standards that let people and AI tools consume governed data. You will work hands-on while building the practices that scale the function as it grows. This is a high-impact role focused on building trust in data, driving adoption and supporting enterprise decision-making.

Requirements

10+ years in enterprise data, including 5+ in data governance with demonstrated leadership
Bachelor’s degree in Information Management, Data Science, Business or a related field

Nice To Haves

Experience with Snowflake, dbt, Power BI and governance tooling
Proven ability to build data governance frameworks, standards and operating models from the ground up
Strong knowledge of governance frameworks (DAMA-DMBOK, DCAM), data management (stewardship, metadata, MDM) and data warehousing/BI
Deep understanding of the data foundations AI requires, quality, lineage, metadata, access control and experience making data AI-ready
Working knowledge of access governance (RBAC/RLS, classification, labeling) and compliance (GDPR, CCPA, SOX, HIPAA)
Proven ability to build and lead executive councils, with strong executive presence and communication

Responsibilities

Own the enterprise data governance standards, processes and operating model, with input from executive sponsors and data teams
Partner with data product managers who apply governance standards in their workstreams, ensuring portfolio consistency
Build and lead executive and cross-functional councils on standards, priorities and tradeoffs
Refine the data roles model (owners, stewards, custodians) and drive governance adoption and data literacy
Set the readiness criteria and standards that make data AI-ready, structured, documented, high-quality and governed for analytics and AI/LLM use
Establish self-service and data management standards so business users and AI tools can safely consume governed data
Define the standards to enable access to data, how users and AI/agent workflows can reach data, how data is made accessible, with audit trails
Support the AI governance model and partner with security, legal and privacy on AI enablement
Establish the framework for data reliability, including the heightened bar AI and ML demand, with monitoring, scorecards and remediation
Oversee metadata, classification, tagging and lineage across the estate (Snowflake, dbt, Power BI), keeping assets discoverable
Partner with platform and data engineering to build the capabilities that operationalize governance for secure, reliable data
Define access governance standards (RBAC/RLS, sensitivity labeling) and controls for material non-public information, including insider/MNPI and compliance