Data Engineering Lead

About OpenSesame While it appears to most people that we just sell training courses (over 50,000 of them), what we really offer is the opportunity for companies to upgrade the skills of each of their employees and reinvent their workforce in an AI world. We have strategic partnerships with 150+ Global 2000 companies who rely on our training programs to develop the world's most productive and admired workforces. Now we are building what comes next. About the Team The Data Engineering Lead will guide a lean and focused data engineering team. The team’s mission is to build and scale an AI-ready data foundation that enables reliable analytics, operational reporting, governance, and future AI initiatives across the company. This role partners closely with analysts, business leaders, platform teams, and data consumers company-wide to improve accessibility, trust, and usability of enterprise data assets. About the Role This role owns the Data Governance & Accessibility program and serves as the technical leader responsible for designing scalable data architecture, improving pipeline reliability, mentoring data engineers, and enabling analysts through robust, specialized data solutions. Success in this role requires balancing hands-on technical leadership with strategic planning, cross-functional influence, and operational excellence. Performance Objectives 30 Days Develop a comprehensive understanding of OpenSesame’s current data ecosystem, governance practices, reporting dependencies, and analyst workflows. Conduct a full assessment of existing pipelines, warehouse architecture, data quality gaps, lineage visibility, and AI-readiness constraints. Establish working relationships with analytics stakeholders across departments and create a prioritized roadmap for improving governance, accessibility, and platform scalability while aligning the two-person engineering team around delivery standards and operating rhythms. 60 Days Design and begin implementing the foundational architecture for an AI-ready data platform, including standardized ingestion patterns, governance controls, metadata management, and scalable transformation frameworks. Introduce monitoring, alerting, and documentation standards that improve reliability and analyst trust in shared datasets. Mentor the Data Engineers through structured technical reviews, backlog prioritization, and development planning while improving delivery velocity and reducing operational bottlenecks for analyst requests. 90 Days Launch the first phase of the Data Governance & Accessibility program by delivering production-ready, well-documented pipelines and certified datasets that support multiple business functions. Establish company-wide standards for data ownership, quality monitoring, lineage tracking, and access management. Demonstrate measurable improvements in analyst efficiency, data reliability, and pipeline performance while creating a sustainable technical mentorship model that elevates engineering quality, promotes knowledge sharing, and prepares the organization for future AI and advanced analytics initiatives. 6 Months Deliver a scalable and extensible data platform capable of supporting advanced analytics, machine learning experimentation, and AI-powered business initiatives. Establish governance processes that enable self-service analytics while maintaining strong security and data quality standards. Create a high-performing engineering culture centered on technical excellence, operational discipline, and continuous improvement, with clear ownership models and repeatable delivery practices across the data organization. Success in the Role Looks Like Analysts across the company can reliably access trusted, well-documented datasets with minimal engineering intervention. Data pipelines operate with strong observability, measurable SLAs, and reduced operational failures. Governance standards are consistently applied across core business domains. The data platform is structured to su…