At Instructure, we believe in the power of people to grow and succeed throughout their lives. Our goal is to amplify that power by creating intuitive products that simplify learning and personal development, facilitate meaningful relationships, and inspire people to go further in their education and careers.
We do this by giving smart, creative, passionate people opportunities to create awesome. And that's where you come in:
Location: Remote
Department: Engineering
Level: Senior Leadership
Employment Type: Full-time
AI-Forward Role
We are seeking a Director of Data & Analytics Engineering to lead our data platform teams and power decision-making across the company. In this senior leadership position, you will own and evolve our end-to-end data platform—from ingestion and transformation to analytics layers that business teams rely on daily. You’ll oversee Data Engineering (infrastructure, pipelines, reliability) and Analytics Engineering (data models, metrics, self-serve tooling), while championing an AI-first approach to the way we build, operate, and innovate.
Platform Leadership: Own the architecture and roadmap for the modern data stack, from source systems through to consumption layers.
Team Building: Hire, grow, and inspire both data engineers and analytics engineers, fostering a culture of quality, curiosity, and ownership.
AI Integration: Embed AI tooling natively into the team’s workflows for build, testing, documentation, and monitoring of our data platform.
Business Partnership: Translate commercial priorities into robust data infrastructure that is agile, trusted, and scalable.
Define and own the multi-year roadmap for the data platform, aligning investments in infrastructure, tooling, and headcount with business strategy.
Lead and grow two high-performing teams—Data Engineering and Analytics Engineering—cultivating a collaborative, feedback-rich environment with clear career pathways.
Architect and oversee scalable data pipelines across ingestion, transformation, orchestration, and delivery, for both batch and streaming use cases.
Champion best practices in analytics engineering, including semantic layer design, dbt modelling standards, data contracts, and metrics governance.
Partner with Data & Decision Science, Product, Finance, and Commercial teams to deliver high-quality, self-serve data solutions aligned to business needs.
Ensure data platform reliability, observability, SLAs, and incident response, treating the platform as a product with real users.
Drive vendor and tool evaluations for the modern data stack (cloud warehouse, orchestration, cataloging, transformation, reverse ETL, etc.).
Set and enforce data quality, documentation, and governance standards to build trust across the business.
AI-assisted development: Champion use of AI coding assistants and LLM-powered tooling (e.g. Cursor, GitHub Copilot, Claude) to accelerate delivery and reduce toil.
Intelligent data pipelines: Implement AI-native patterns—LLM-generated documentation, anomaly detection, data quality monitoring, and automated root-cause analysis.
Natural language interfaces: Prototype NL-to-SQL and AI-powered BI tools to empower self-serve analytics for non-technical users.
AI platform enablement: Build foundational data infrastructure (feature stores, vector stores, model metadata, evaluation datasets) to enable AI and ML experimentation and scale.
7+ years in data engineering or analytics engineering, with 3+ years in a senior leadership role managing multiple teams
Deep expertise in the modern data stack—cloud data warehouses (Snowflake, BigQuery, or Databricks), dbt, orchestration tools (Airflow, Dagster, or Prefect), and ELT frameworks
Proven ability to define and execute a multi-year data platform strategy
Strong stakeholder management, including executive presentations and translating technical concepts to non-technical audiences
Experience building and scaling high-performing engineering teams: hiring, mentoring, performance management
Track record of delivering trusted, well-documented, and widely adopted data products
It would be great if you also had:
Hands-on experience integrating AI/LLM tooling into engineering workflows or data products
Familiarity with semantic layer tools (e.g. MetricFlow, Cube), data cataloging (e.g. Atlan, Datahub), and data observability platforms
Experience with streaming data (Kafka, Flink, or Kinesis) and batch processing
Knowledge of ML infrastructure: feature stores, model serving, vector databases
Exposure to data mesh or data product organizational models
Strong command of SQL and Python
We believe the best data leaders are equal parts engineer, strategist, and people developer. If you are energized by the intersection of platform craft and AI, we encourage you to apply.
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
Annual learning and development stipends 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.