Job description

Working at Atlassian Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity. Data is a big deal at Atlassian. We ingest billions of events each month into our analytical platforms, and dozens of teams across the company depend on that data to run the business. As AI systems increasingly consume our data alongside our teams, the standard has risen: data must be clearly described, accurately classified, explicitly owned, and demonstrably trustworthy before it can be put to use Atlassian is looking for an Engineering Leader to join the Data Engineering team and own data governance and the enterprise context that makes our data trusted and AI-ready. You will own the enterprise data governance operating model — domain ownership, decision rights, the governance council, and the steward network — setting policy for classification, access, retention, and acceptable use with Privacy, Legal, Security, and Compliance, and running data quality as a product . In parallel, you will shape the technical strategy and architecture for our data catalog, metadata, and lineage platform, and build the enterprise context that connects business entities to their ownership, lineage, and quality signals — exposed programmatically so that agents and applications consume trusted context . You will shift governance left into data contracts, automated classification at ingest, and policy-as-code, define AI readiness as a certifiable state that continuously gates what data our models and agents can use, drive adoption across a federated organization. • 15+ years of experience in data engineering, data platform, or data management, with a focus on data strategy, architecture, governance, and platform buildout • 8+ years of experience leading data engineering teams, including leading senior and principal-level architects and landing architecture through teams outside your reporting line • Demonstrated experience standing up or scaling an enterprise data catalog or metadata platform — DataHub, Unity Catalog, or equivalent — owning integration, adoption, and outcome metrics rather than tool administration • Demonstrated experience owning a data governance operating model end to end: policy, domain ownership, stewardship, classification, data quality, and controls that held up under 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 intended for machine consumption, including agent-facing metadata access such as MCP servers, semantic search endpoints, or context APIs • Experience across both corporate data domains (Finance, People, GTM) and customer or product data, with judgment about how their risk profiles differ • Working fluency in privacy and regulatory requirements — GDPR, CCPA/CPRA, SOX, SOC 2 — and practical command of classification schemes, access control models (RBAC/ABAC), masking, tokenization, and purpose limitation • Technical depth to lead a design review with a principal engineer: strong SQL, modern lakehouse platforms (Snowflake, Databricks, BigQuery), transformation tooling such as dbt, orchestration, and streaming/CDC patterns • Experience implementing DevOps best practices within the data platform, including CI/CD, data observability, monitoring, and lineage • Experience building services and RESTful APIs, and exposing metadata or platform capability programmatically for machine consumption • Demonstrated experience developing influential relationships and trust with senior leaders across different departments, and driving voluntary adoption from data producers who do not report to you • Prior experience partnering with Analytics, Data Science, Engineering, Security, Privacy, Legal and Product Management teams • A graduate degree in Computer Science, Information Management or a similar subject area It's great but not required if you have: • Experience applying LLMs to governance work itself — 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 • Committed code to open source projects Benefits & Perks Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits . About Atlassian At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together. We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines. To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them. To learn more about our culture and hiring process, visit go.atlassian.com/crh . In line with local law, identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes. At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range. Please visit go.atlassian.com/payzones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter. This role may also be eligible for benefits, bonuses, commissions, and equity.

Requirements

Must-haves

Working at Atlassian
Atlassian is looking for an Engineering Leader to join the Data Engineering team and own data governance and the enterprise context that makes our data trusted and AI-ready.
15+ years of experience in data engineering, data platform, or data management, with a focus on data strategy, architecture, governance, and platform buildout
8+ years of experience leading data engineering teams, including leading senior and principal-level architects and landing architecture through teams outside your reporting line
Demonstrated experience standing up or scaling an enterprise data catalog or metadata platform — DataHub, Unity Catalog, or equivalent — owning integration, adoption, and outcome metrics rather than tool administration
Demonstrated experience owning a data governance operating model end to end: policy, domain ownership, stewardship, classification, data quality, and controls that held up under 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 intended for machine consumption, including agent-facing metadata access such as MCP servers, semantic search endpoints, or context APIs
Experience across both corporate data domains (Finance, People, GTM) and customer or product data, with judgment about how their risk profiles differ
Working fluency in privacy and regulatory requirements — GDPR, CCPA/CPRA, SOX, SOC 2 — and practical command of classification schemes, access control models (RBAC/ABAC), masking, tokenization, and purpose limitation
Technical depth to lead a design review with a principal engineer: strong SQL, modern lakehouse platforms (Snowflake, Databricks, BigQuery), transformation tooling such as dbt, orchestration, and streaming/CDC patterns
Experience implementing DevOps best practices within the data platform, including CI/CD, data observability, monitoring, and lineage
Experience building services and RESTful APIs, and exposing metadata or platform capability programmatically for machine consumption
Demonstrated experience developing influential relationships and trust with senior leaders across different departments, and driving voluntary adoption from data producers who do not report to you
Prior experience partnering with Analytics, Data Science, Engineering, Security, Privacy, Legal and Product Management teams
A graduate degree in Computer Science, Information Management or a similar subject area
It's great but not required if you have:
Experience applying LLMs to governance work itself — 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
Committed code to open source projects

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aisqlci/cdgo

Platform: atlassian
Effort: medium