Role Description Turnitin is looking for an experienced Enterprise Data Architecture & Governance Lead to help define the future of how we use data to run our business. This role represents a unique opportunity to shape Turnitin's enterprise data strategy by establishing the foundations for trusted, scalable and decision-ready information. What you’ll do: - Assess Turnitin's current reporting and analytics landscape; - Identify the root causes of fragmented reporting and conflicting metrics; - Define ownership and governance principles for critical enterprise data; - Develop a high-level target-state architecture for enterprise reporting and analytics; - Establish standards for enterprise metric definitions and trusted reporting; - Recommend an operating model that balances enterprise consistency with functional autonomy; - Produce a practical roadmap for future implementation and investment; - Facilitate alignment across executive and business stakeholders. What success looks like: - A shared understanding of its current data landscape; - Clear ownership of priority enterprise metrics; - Improved confidence in Executive and Board reporting; - A pragmatic governance framework; - A target-state blueprint for enterprise reporting and analytics; - A prioritised roadmap for implementation. Qualifications - 10+ years of experience in enterprise data governance, architecture, analytics governance, or enterprise reporting controls. - Experience operating across Finance, Sales, Revenue Operations, HR, Engineering, Product, and Business Intelligence functions. - Strong understanding of data lineage, data quality controls, source-system reconciliation, data transformation processes, and reporting controls. - Experience supporting Executive, Board & Investor Reporting. - Experience defining governance frameworks, ownership models, and metric standards. - Demonstrated experience facilitating cross-functional agreement on enterprise metric definitions and ownership. - Experience designing operating models for enterprise data functions. - Familiarity with privacy considerations relating to the use of customer and employee data. - Strong stakeholder management and facilitation skills. - Ability to communicate effectively with both technical and non-technical stakeholders. - Strong documentation, process design, and executive communication skills. Requirements - Experience in SaaS, subscription businesses or distributed analytics environments would be highly advantageous. Benefits - Remote First Culture - Health Care Coverage* - Education Reimbursement* - Competitive Paid Time Off - Self-Care Days - National Holidays* - 2 Founder Days + Juneteenth Observed - Paid Volunteer Time* - Charitable contribution match* - Monthly Wellness or Home Office Reimbursement/* - Access to Modern Health (mental health platform) - Parental Leave* - Retirement Plan with match/contribution* * varies by country

• Architect a greenfield, multi-layer data warehouse (raw, refined, serving) that separates analytical workloads from production OLTP traffic. • Deliver a governed, self-service data-access layer for internal consumers first (Product, CSM, Deployment/Operations, and Leadership) as Phase 1, ahead of customer-facing conversational analytics. • Build a semantic and metrics layer so every metric, such as "scan accuracy by site," is defined once in code and stays identical across every dashboard and product, making self-service safe from metric drift. • Own the quality bar: 99%+ availability SLA with freshness guarantees, 100% traceability, zero cross-tenant leakage, 99.5%+ pipeline success, and no data loss.  • Design tenant isolation, per-tenant cost attribution, and schema and row-level RBAC to scale toward hundreds of tenants (300+ target), not today's fleet size. • Own data-ingestion correctness at the boundary with the integration/backend team, covering data contracts, schema validation, and pipeline quality, so WMS data lands in the right place, shape, and time across WMS versions. • Stand up a data catalog and lineage layer (Purview as the Azure-native fit, DataHub as the open-source alternative) so every consumer can find data, see ownership, and trace lineage when a metric looks wrong. • Prove the foundation end to end on Gather's drone product, then generalize it so each new product extends the model instead of rebuilding it • Act as the connective tissue between product and ML (3DCC, damage detection). Link structured records to unstructured drone imagery and video with full traceability, and stand up the data-infra readiness for feature stores and annotation pipelines on one trusted foundation.

• Define company data assets (data model) • Define and design data integrations, data quality frameworks and design and evaluate open source/vendor tools for data lineage • Work closely with Dropbox business units and engineering teams to develop strategy for long term Data Platform architecture to be efficient, reliable and scalable • Conceptualize and own the data architecture for multiple large-scale projects, while evaluating design and operational cost-benefit tradeoffs within systems • Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights in a meaningful way • Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains • Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts

• Lead a team of 2-3 engineers • Working with fellow Nava engineers to design, review, and build well-crafted software • Working in an agile manner to efficiently ship new features that meet user needs • Design and develop scalable data ingestion and processing pipelines • Implement large-scale data ecosystems within cloud-based platforms that include data management and data governance of structured and unstructured data • Implement data validation and quality checks to ensure accuracy and consistency • Work with cross-functional project teams to gather business requirements and translate to detailed technical specifications • Work with Government partners to assist and develop data engineering applications and pipelines that will enable data services and processing capabilities • Participate in software design and code reviews • Maintain security and privacy standards in all aspects of the data pipeline • Taking part in hiring activities (e.g., submitting referrals, conducting interviews, and attending interview debriefs), as needed