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