Delivery Lead with Data Engineer

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
  
• Strong
  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