Head of Data Governance & Enterprise Context Eng

Atlassian•San Francisco, CA

8h•$197,955 - $311,375

About The Position

Atlassian is seeking an Engineering Leader to join its Data Engineering team and own data governance and the enterprise context that makes data trusted and AI-ready. The role is responsible for the enterprise data governance operating model, including domain ownership, decision rights, the governance council, and the steward network. It sets policies for classification, access, retention, and acceptable use in partnership with Privacy, Legal, Security, and Compliance, and treats data quality as a product. The leader will shape the technical strategy and architecture for the data catalog, metadata, and lineage platform, and build enterprise context that connects business entities to ownership, lineage, and quality signals. This context will be made available programmatically for agents and applications. The role will move governance earlier into data contracts, automated classification at ingest, and policy-as-code; define AI readiness as a certifiable state that continuously gates data use by models and agents; and drive adoption across a federated organization. Atlassians may choose to work in an office, from home, or a combination of the two. The company can hire in countries where it has a legal entity. The role may be eligible for benefits, bonuses, commissions, and equity. U.S. base salary ranges are $238,500–$311,375 in Zone A, $214,650–$280,238 in Zone B, and $197,955–$258,441 in Zone C.

Requirements

15+ years of experience in data engineering, data platform, or data management, focused on data strategy, architecture, governance, and platform buildout.
8+ years of experience leading data engineering teams, including senior and principal-level architects, and landing architecture through teams outside the reporting line.
Demonstrated experience standing up or scaling an enterprise data catalog or metadata platform such as DataHub, Unity Catalog, or equivalent, including ownership of integration, adoption, and outcome metrics.
Demonstrated end-to-end ownership of a data governance operating model covering policy, domain ownership, stewardship, classification, data quality, and controls that withstood audit or regulatory scrutiny.
Experience with a data-as-a-product or data contracts operating model.
Experience building a semantic layer, ontology, or knowledge graph for machine consumption, including agent-facing metadata access such as MCP servers, semantic search endpoints, or context APIs.
Experience across corporate data domains such as Finance, People, and GTM, as well as customer or product data, with judgment about their differing risk profiles.
Working fluency in GDPR, CCPA/CPRA, SOX, and SOC 2 requirements, and practical command of classification schemes, RBAC/ABAC access controls, masking, tokenization, and purpose limitation.
Technical depth to lead a design review with a principal engineer, including strong SQL, modern lakehouse platforms such as Snowflake, Databricks, or BigQuery, transformation tooling such as dbt, orchestration, and streaming/CDC patterns.
Experience implementing data-platform DevOps practices, including CI/CD, data observability, monitoring, and lineage.
Experience building services and RESTful APIs and exposing metadata or platform capabilities programmatically for machine consumption.
Demonstrated ability to develop influential relationships and trust with senior leaders across departments and drive voluntary adoption by data producers outside the reporting line.
Prior experience partnering with Analytics, Data Science, Engineering, Security, Privacy, Legal, and Product Management teams.
Graduate degree in Computer Science, Information Management, or a similar subject area.

Nice To Haves

Experience applying LLMs to governance work, such as metadata and description generation, classification, entity resolution, or data quality anomaly triage.
Experience working for SaaS companies, including multi-tenant customer data, tenant isolation, and data residency at scale.
Experience with responsible AI programs or AI governance frameworks such as the NIST AI RMF, ISO/IEC 42001, or the EU AI Act.
DAMA-DMBOK grounding, CDMP, or a privacy certification such as CIPP or CIPT.
Contributions of code to open-source projects.

Responsibilities

Own the enterprise data governance operating model, including domain ownership, decision rights, the governance council, and the steward network.
Set policies for data classification, access, retention, and acceptable use in partnership with Privacy, Legal, Security, and Compliance.
Run data quality as a product.
Shape the technical strategy and architecture for the data catalog, metadata, and lineage platform.
Build enterprise context connecting business entities to ownership, lineage, and quality signals, and expose it programmatically for agents and applications.
Shift governance left through data contracts, automated classification at ingest, and policy-as-code.
Define AI readiness as a certifiable state that continuously gates which data models and agents can use.
Drive adoption across a federated organization.