Data Engineering Lead

OpenSesame is a company focused on upgrading employee skills through training programs. They are seeking a Data Engineering Lead to guide a data engineering team in building an AI-ready data foundation that enhances data accessibility and governance across the organization.

Responsibilities:

• 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
• 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
• 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
• 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
• 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 support future AI and machine learning initiatives without major architectural redesign
• The engineering team demonstrates improved delivery consistency, technical maturity, and collaborative execution