Director, Enterprise Data Management & Governance

Pluralsight

8d•$167,200 - $220,000•Hybrid

About The Position

The Director, Data Management and Governance owns the company-wide standards, controls, and accountability that make Pluralsight's data trustworthy. This leader sets and enforces data governance policy across the data org (Data Engineering and Analytics Engineering), the upstream teams that produce data, and the external SaaS vendor data owners (e.g., Salesforce) the company depends on — including data contracts enforced as a gate before data is ingested. This is a hands-on, player-coach role: the right leader sets policy and grows the team, but also profiles data, writes the queries, and drafts the contracts themselves. They are also a fluent, productive user of AI in their own day-to-day work, both to accelerate the governance practice and to model the AI-augmented way of working they are asking the team to adopt.

Requirements

Requires a minimum of 12 years of related or equivalent experience; or 8+ years and an advanced degree.
Expertise in data management and governance — data contracts, metadata management, master data management, data quality, and policy — with the ability to tie standards and controls to business value and to customer trust.
Demonstrated ability to set and enforce company-wide standards and negotiate data ownership across internal engineering teams, upstream data producers, and external SaaS vendor owners.
Experience implementing data contracts and a schema/version registry as an enforced, pre-ingestion control.
Current, hands-on technical depth — able to profile data, write and review SQL personally, and author and assess data contracts and schemas.
Demonstrated, productive use of AI in day-to-day data work — using AI tools fluently to accelerate investigation, documentation, and analysis, with the judgment to do so safely on governed data.
Strong grasp of PII classification, masking, and role-based access control, particularly as prerequisites for safe AI access to data.
Experience leading data management and governance programs in the context of larger business and digital transformations.
Ability to communicate standards, metrics, and trade-offs clearly to senior leaders and to technical and business teams alike.

Nice To Haves

Familiarity with the modern data stack this function governs (e.g., Snowflake, dbt) and with major SaaS sources such as Salesforce, Zuora, and Workday.
Experience standing up PII/RBAC controls as a prerequisite for enterprise AI enablement.
Experience governing data across a SaaS-heavy enterprise landscape with multiple external data owners.

Responsibilities

Set, own, and enforce company-wide data governance policy and standards spanning the data org (Data Engineering and Analytics Engineering), upstream data producers, and external SaaS vendor data owners (e.g., Salesforce) — the standards, controls, and accountability that keep data consistent and trustworthy at the source.
Establish and enforce data contracts as a gate before ingestion — defining producer obligations for schema, semantics, quality, and change notification.
Own the schema-registration and versioned change-control process.
Drive the principle of centralizing data transformation logic in the data org's data model layer.
Lead PII governance end to end — identifying, classifying, tagging, and masking sensitive data, and setting the role-based access controls (RBAC).
Establish data-quality standards, measurement, and monitoring — defining the metrics, making data health visible.
Own master data management for core enterprise entities — accounts, contacts and de-duplication, and similar — as the authoritative, reconciled source of truth.
Lead, develop, and grow the data governance and data quality team, setting priorities and standards while sustaining day-to-day data-quality operations without interruption.
Stay hands-on in the work: profile and investigate data directly, write the queries, draft and review the data contracts and policy, and take direct ownership of the hardest data-quality and data-ownership problems.
Use AI productively in day-to-day governance work — accelerating data-quality investigation, policy and contract drafting and review, and data profiling — and set the example for AI-augmented working across the team.