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Foodstuffs North Island Ltd

Domain Lead - Data Engineering

Engineering LeadershipPosted on Nov 10
Full-timeAuckland, New ZealandNo salary listed

About the Job

Requirements

  • Significant leadership experience across enterprise-scale data engineering, data platforms, analytics engineering or data architecture.
  • Deep understanding of modern data architecture and engineering, including cloud data platforms, data warehouses, data lakes and lakehouse, data fabric, data mesh, and modern integration patterns.
  • Strong experience establishing data engineering standards, patterns and guardrails that improve quality, security and consistency without creating unnecessary barriers for delivery teams.
  • Strong experience with data-as-a-product, including building trusted, governed, discoverable and reusable data assets for enterprise use.
  • Experience improving engineering maturity across data quality, DataOps, continuous integration and continuous delivery, pipeline reliability, observability, metadata and security by design.
  • A strong understanding of data governance, privacy, lineage, ownership and data quality, and how these work alongside engineering to create trust in data.
  • Experience working with enterprise data and analytics technologies such as Google BigQuery or equivalent cloud data platforms, SAP Datasphere, Power BI, SAP Analytics Cloud, and data orchestration and integration technologies.
  • Proven ability to balance engineering excellence, business value, governance, commercial realities and long-term platform sustainability.
  • Experience supporting the strategic direction, health and investment of enterprise data platforms, including lifecycle management, scalability and cost optimisation.

Responsibilities

  • Set the direction for modern data engineering by establishing practical standards, guardrails, patterns and reference architectures across the domain.
  • Lead through technical leaders, empowering Principal Engineers, Tech Leads and Chapter Leads to shape technical detail while maintaining clear accountability for domain outcomes.
  • Champion data-as-a-product, enabling teams to create data that is trusted, reusable, discoverable, well governed and valuable to the business.
  • Drive modern data engineering practices, including DataOps, automation, continuous integration and continuous delivery, data quality, observability, metadata and lineage.
  • Shape the approach to modern data platforms, including cloud data platforms, lakehouse, data fabric, data mesh, semantic modelling and governed self-service analytics.
  • Balance autonomy with consistency, giving teams room to innovate while ensuring enterprise standards for security, quality, governance and engineering are maintained.
  • Build and develop the Data engineering capability by strengthening technical leadership, career pathways, communities of practice and succession across the domain.

About the Company

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