Delivery
Manager – Data Engineering Programs

Role
Summary

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.

Key
Responsibilities

Delivery
Leadership

- 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.

Data
Engineering & 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.

People
Leadership

- 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.

Required
Qualifications

- 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.

Technical
Expertise

Data
Platforms

- Microsoft
Azure

- Microsoft
Fabric

- Azure
Data Factory (ADF)

- Azure
Synapse Analytics

- SQL
Server

- Power
BI

Data
Engineering

- 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

Essential
Skills

- Programme
leadership

- Executive
stakeholder management

- Client
relationship management

- Risk
and issue management

- Solid
communication and presentation skills

- Team
building and people leadership

- Negotiation
and conflict resolution

- Outcome
ownership and execution excellence

Success
Measures

- 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

📌 Delivery Lead with Data Engineer (Melbourne)
🏢 Recognized
📍 Melbourne