Delivery Lead with Data Engineer at XPT Software… · Melbourne

DeliveryManager – Data Engineering Programs

RoleSummary

We are seeking an experienced Delivery Manager to lead the successful delivery of large-scale Data Engineering programs. The role requires ownership of end-to-end delivery, including planning, execution, governance, stakeholder engagement, and team leadership. The ideal candidate will possess a strong background in cloud data platforms, data warehousing, data engineering practices, and programme delivery management.

The Delivery Manager will act as the primary interface between business stakeholders, clients, architecture teams, and engineering teams to ensure high-quality, on-time, and on-budget delivery of strategic data initiatives.

KeyResponsibilities

DeliveryLeadership

• Own end-to-end delivery of Data Engineering programs, ensuring alignment with business objectives and client expectations.
• Establish programme governance, delivery frameworks, reporting cadence, and risk management processes.
• Manage scope, schedule, budget, quality, and resource planning across multiple concurrent initiatives.
• Ensure successful delivery of data engineering initiatives.
• Drive Agile, Scrum, and DevOps delivery practices across distributed teams.

Client& Stakeholder Management

• Serve as the primary point of contact for business and technology stakeholders.
• Build trusted relationships with client leadership and internal executives.
• Facilitate programme reviews, and executive reporting.
• Translate business requirements into executable delivery roadmaps.

DataEngineering & Technology Oversight

• Provide delivery oversight for enterprise data platforms built on Azure, Microsoft Fabric, Synapse.

• Ensure engineering teams adhere to architecture standards, security requirements, and best practices.

• Oversee implementation of:

  • Data Warehousing and Lakehouse solutions
  • ETL/ELT pipelines
  • Data quality and governance frameworks
  • AI initiatives

• Partner with architects to drive scalable, secure, and cost-effective solutions.

Financial& Commercial Management

• Manage contract commitments, resource utilization, and delivery KPIs.
• Identify opportunities for account growth, service expansion, and value realization.

Risk& Quality Management

• Establish delivery assurance practices.
• Proactively identify programme risks, dependencies, and mitigation plans.
• Ensure compliance with governance, security, regulatory, and audit requirements.
• Drive continuous improvement across delivery processes and operational metrics.

PeopleLeadership

• Build, mentor, and lead high-performing Data Engineering teams.
• Support hiring, onboarding, workforce planning, performance management, and career development.
• Foster a culture of accountability, collaboration, innovation, and continuous learning.

RequiredQualifications

• 12+ years of IT experience with at least 5+ years managing large Data Engineering or Analytics programs.
• Proven experience leading enterprise-scale data transformation initiatives.
• Strong understanding of modern data architectures and cloud-native platforms.
• Experience managing geographically distributed delivery teams.

TechnicalExpertise

DataPlatforms

• Microsoft Azure
• Microsoft Fabric
• Azure Data Factory (ADF)
• Azure Synapse Analytics
• SQL Server
• Power BI

DataEngineering

• Data Warehousing
• Lakehouse Architecture
• Data Modeling
• ETL/ELT Frameworks
• Data Governance
• Data Quality Management
• Performance Optimization

Delivery& Operations

• Agile / Scrum
• Azure DevOps / Jira
• Release & Change Management

EssentialSkills

• Programme leadership
• Executive stakeholder management
• Client relationship management
• Risk and issue management
• Strong communication and presentation skills
• Team building and people leadership
• Negotiation and conflict resolution
• Outcome ownership and execution excellence

SuccessMeasures

• On-time and on-budget programme delivery
• Client satisfaction and CSAT improvement
• Revenue and margin achievement
• Delivery quality and defect reduction
• Team engagement and retention
• Adoption of engineering best practices
• Continuous improvement and innovation outcomes