Original listing text, shown exactly as published by the company.
What you will do
- Define and own the enterprise data architecture strategy, including conceptual, logical, and physical data models across the organization's core domains
- Establish and govern data standards, naming conventions, schema design principles, and modeling best practices used by Data Engineering and Analytics Engineering teams
- Lead the design of scalable, reusable data products in the semantic and analytical layers, ensuring consistency across Decision Science, Data Science, and self-service consumption
- Partner with the Product Data team to align on shared architectural standards, data contracts, and platform decisions—acting as a peer and collaborator, not a dependency
- Evaluate and advise on data platform and tooling decisions (cloud data warehouses, lakehouse patterns, orchestration, metadata management, cataloging)
- Identify and resolve architectural gaps, redundancy, and data quality risks across the data estate
- Contribute to—and in many cases lead—the development of a business glossary, data catalog, and enterprise ontology for key data domains
- Act as a senior advisor to Data Science on data availability, feature engineering infrastructure, and model data requirements
- Collaborate with Decision Science leadership to ensure analytical data models are structured for performance, clarity, and governed self-service
- Champion data governance, lineage, and observability as first-class architectural concerns
- Mentor and guide engineers and analytics engineers on architectural patterns and data modeling best practices
Who You'll Work WithWithin Business Data & Insights
- Data Engineering — pipeline design, ingestion standards, storage architecture
- Analytics Engineering — dbt modeling, semantic layer, data mart design
- Data Science — feature stores, training data infrastructure, model serving data
- Decision Science — reporting layer architecture, metric definitions, performance
Cross-functional Partnership
- Product Data Team — shared standards, data contracts, source alignment
- Product and Engineering Executives — architectural roadmap, governance, strategic investment
- Engineering / Platform teams — infrastructure alignment and platform APIs
- Legal & Compliance — data privacy, retention, and classification architecture
What you'll need to know/have
- 8+ years of experience in data architecture, data engineering, or a closely related discipline in a complex, multi-team data environment
- Demonstrated experience designing and governing enterprise data models across transactional, analytical, and semantic layers
- Deep expertise in modern data stack patterns: cloud data warehouses (Snowflake, BigQuery, Databricks), lakehouse architectures, dbt, data cataloging tools
- Strong command of data modeling methodologies—dimensional modeling, Data Vault, OBT, and when to apply each
- Experience establishing or evolving data governance programs including metadata management, lineage, and data quality frameworks
- Ability to work across technical and business stakeholders—translating architectural decisions into clear business value
- Experience partnering with Data Science teams on feature engineering, training datasets, or MLOps data infrastructure
- Excellent communication and documentation skills; you write clearly about architecture for both technical and executive audiences
- Experience working in matrix or cross-functional environments, navigating organizational boundaries without direct authority
It would be a bonus if you also had
- Experience in a company with both a centralized data function and an embedded Product Data or Data Platform team
- Familiarity with semantic layer tools (Cube, MetricFlow, LookML) and headless BI patterns
- Background in data mesh, data product, or federated data governance operating models
- Exposure to real-time or streaming data architecture (Kafka, Flink, Spark Streaming)
- Experience with data privacy-by-design architecture and regulatory frameworks (GDPR, CCPA)
What Sets You Apart
- You think in systems: You design for the entire data lifecycle—not just the next sprint—and can articulate the long-term tradeoffs of every architectural choice.
- You lead through influence: You don't need a reporting line to drive alignment. You build credibility through expertise, communication, and a clear point of view.
- You're a bridge-builder: You thrive operating between teams—Business and Product Data, engineering and the business—and know how to find the win-win.
- You balance vision and pragmatism: You have strong architectural opinions but know how to phase delivery, make tradeoffs, and ship real value while keeping the north star in view.
Get in on all the awesome at Instructure!
We offer competitive, meaningful benefits in every country where we operate. While they vary by location, here's a general idea of what you can expect:
- Competitive compensation, plus all full-time employees participate in our ownership program - because everyone should have a stake in our success.
- Flexible work culture. Our remote, hybrid and in-office collaboration spaces vary by role, team and location.
- Generous time off, including local holidays and our annual “Dim the Lights” period in late December, when teams are encouraged to step back and recharge based on departmental needs.
- Comprehensive wellness programs and mental health support
- Learning and development resources, including professional development tools and tuition reimbursement, to support your growth
- The technology and tools you need to do your best work
- Motivosity employee recognition program
- A culture rooted in inclusivity, support, and meaningful connection
We believe in hiring great people and treating them right. The more diverse we are, the better our ideas and outcomes.
Instructure is an Equal Opportunity Employer. We comply with applicable employment and anti-discrimination laws in every country where we operate.
All employees must pass a background check as part of the hiring process. To help protect our teams and systems, we’ve implemented identity verification measures. Candidates may be asked to verify their legal name, current physical location, and provide a valid contact number and residential address, in accordance with local data privacy laws.
Any attempt to misrepresent personal or professional information will result in disqualification.
