Director, Data & Analytics Engineering

Director, Data & Analytics Engineering 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: DIRECTOR, DATA & ANALYTICS ENGINEERING Location: Remote Department: Engineering Level: Senior Leadership Employment Type: Full-time AI-Forward Role ABOUT THE 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. 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. WHAT YOU WILL DO: - 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. WHAT YOU'LL NEED TO KNOW/HAVE: - 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 ... (truncated, view full listing at source)