Director of Data & Analytics Engineering, Instructure

• 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
• 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

Desirable

• 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
• 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
• Four Pillars of This Role:
• 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