Lead Analytics Engineer & Data Platform Lead
Irving Shipbuilding
We are seeking a Lead Analytics Engineer & Data Platform Lead to provide technical leadership through the evolution of analytics across the Sawmills Division.
This role will lead the development and stewardship of the division's analytics foundations, including semantic models, data engineering, operational data integration, reporting platforms, and modernization initiatives. The successful candidate will help guide the division through an evolving analytics landscape that includes legacy platforms, enterprise data products, divisional data products, industrial data systems, automation, and emerging AI-enabled capabilities.
The successful candidate combines strong technical expertise with business acumen and serves as a trusted advisor to business stakeholders, technology teams, and analytics resources across the division.
What Success Looks Like
• Trusted and reusable analytics foundations are established and adopted across the division.
• Reporting consistency, governance, scalability, and performance improve over time.
• Operational and enterprise data assets are effectively integrated and leveraged.
• Technical debt is reduced through modernization, rationalization, and standardization efforts.
• Analytics platforms are positioned to support future reporting, automation, and AI capabilities.
• Sustainable documentation, governance, and knowledge-management practices reduce organizational dependency on individual contributors.
• Design, develop, and maintain scalable analytics architectures, semantic models, and reusable data assets.
• Lead data engineering, data transformation, and integration initiatives supporting reporting, analytics, automation, and AI-enabled solutions.
• Support integration of operational and manufacturing systems, including AVEVA and other industrial data platforms, into the analytics ecosystem.
• Provide technical leadership for reporting modernization initiatives involving Cognos, Alteryx, Power BI, Tableau, enterprise semantic models, data lakes, and emerging analytics technologies/platforms.
• Evaluate opportunities to consolidate, modernize, integrate, retain or retire analytics solutions based on business value and technical considerations.
• Collaborate with Corporate Data and Technology teams to align divisional solutions with enterprise data products, semantic models, governance frameworks, and data-platform strategies.
• Establish standards and best practices related to data modelling, semantic modelling, reporting architecture, governance, and analytics development.
• Improve reporting consistency, data quality, performance, scalability, and maintainability across analytics solutions.
• Act as steward of divisional analytics platforms, semantic models, and reusable data products.
• Document and institutionalize analytics standards, business logic, architecture decisions, and technical knowledge.
• Provide mentoring and technical guidance to team members and stakeholders.
• Support emerging technologies evaluation and recommend practical opportunities to improve analytics capability and business value.
Required
• Bachelor's degree in Analytics, Business, Engineering, Computer Science, Mathematics, Statistics, or related field.
• 5+ years of experience delivering enterprise analytics, business intelligence, data engineering, or reporting solutions.
• Advanced expertise in SQL, data modelling, semantic modelling, and analytics architecture.
• Experience designing scalable analytics and reporting solutions.
• Strong analytical, problem-solving, and stakeholder engagement skills.
• Ability to communicate technical concepts effectively to both technical and non-technical audiences.
Preferred
• Experience with Power BI, AVEVA, Tableau, Alteryx, Cognos, Microsoft Fabric, Azure Data Services, or similar platforms.
• Experience supporting manufacturing, operational technology, production planning, quality management, supply chain, forestry products, or industrial operations.
• Experience integrating operational and enterprise data sources.
• Experience supporting reporting modernization, automation, or AI-enabled analytics initiatives.
• Experience with governance, data-quality, or master-data-management practices.
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