JOB DESCRIPTION
The Head of Data Engineering leads the consolidated Data Engineering capability across IT and the former Data and Analytics Centre of Excellence. The role is accountable for the design, build, operation and continuous improvement of enterprise data engineering and machine learning engineering capabilities. Your Opportunity

JOB DESCRIPTION
The Head of Data Engineering leads the consolidated Data Engineering capability across IT and the former Data and Analytics Centre of Excellence. The role is accountable for the design, build, operation and continuous improvement of enterprise data engineering and machine learning engineering capabilities. The role brings foundational and derived data engineering into one integrated function, creating trusted, reusable and ready-to-consume data products that support operational workloads, reporting, analytics, data science and AI delivery. Working closely with Architecture, Data Analytics & Reporting, AI Delivery, Data Governance & AI Mobilisation and business stakeholders, the role establishes consistent engineering standards, strengthens platform reliability and accelerates the safe and scalable use of data across TPG Telecom. You’ll Make Impact By Leadership & Capability
- Lead and develop the consolidated Data Engineering function, bringing together IT BAU engineering and former CoE data and ML engineering capability under explicit accountabilities and ways of working.
- Build a collaborative, high-performing team capable of delivering and operating enterprise-scale data products, pipelines and engineering services.
- Establish effective interfaces with Data Analytics & Reporting, AI Delivery, Architecture, Data Governance & AI Mobilisation and Technology Operations.
- Develop workforce, sourcing,



succession and capability plans aligned to the future data platform roadmap and delivery portfolio. Data Engineering Strategy & Operating Model
- Develop and maintain the enterprise Data Engineering strategy, roadmap and operating model in alignment with the Data Platforms & AI Delivery strategy.
- Consolidate data engineering standards, practices, tooling and governance across previously separate IT and CoE teams.
- Define how foundational, curated and derived data products are created, owned, operated and consumed across the organisation.
- Prioritise engineering capacity across BAU, regulatory, transformation, analytics and AI demand using transparent portfolio and capacity management practices. Data Platforms, Pipelines & Products
- Lead the design, build and operation of reliable, secure and scalable cloud-based data pipelines, ELT/ETL solutions and data products.
- Ensure foundational data products are transformed into trusted, reusable and ready-to-consume datasets that support a consistent source of truth.
- Establish engineering standards for data ingestion, transformation, modelling, orchestration, metadata, lineage, observability and release management.
- Ensure data products meet agreed quality, availability, performance, security, resilience and support requirements.
- Manage the lifecycle of data engineering assets, including technical debt, optimisation, remediation and decommissioning. ML Engineering & AI Enablement




- Lead ML engineering capability required to industrialise, deploy, monitor and optimise approved machine learning and AI solutions.
- Partner with Data Analytics & Reporting and AI Delivery teams to move validated prototypes and use cases into scalable production services.
- Establish repeatable MLOps and engineering practices covering deployment, monitoring, model performance, versioning and operational support.
- Ensure AI and ML engineering solutions comply with architecture, security, privacy, data governance and responsible AI requirements. Delivery, Operations & Performance
- Own a portfolio of ongoing and project-based data engineering work, with clear outcomes, milestones, dependencies, risks and resource requirements.
- Ensure engineering services transition into sustainable operational ownership with clear support models, service expectations and controls.
- Establish meaningful measures for delivery performance, platform reliability, data quality, engineering productivity and customer outcomes.
- Use performance, incident and stakeholder insights to drive continuous improvement and prioritise remediation.
- Manage budgets, suppliers, delivery partners and commercial commitments within agreed constraints. Data Governance, Risk & Stakeholder Management
- Partner with Data Governance & AI Mobilisation to embed data ownership, quality, metadata, lineage, access and retention requirements into engineering practices.
- Build trusted relationships with business and Technology stakeholders and translate data needs into practical engineering priorities and solutions.
- Ensure material delivery, operational, security, privacy, data quality and technology risks are identified, controlled and approp

📌 Head of Data Engineering (Sydney)
🏢 Tpg Telecom
📍 Sydney