Technical Manager/ Data Project Manager (Lead Data engineer)

Unison Group

Lead and manage large-scale Data Engineering and Data Modernization projects.
Drive end-to-end delivery of Data Lake build and migration initiatives.
Lead PySpark migration and optimization projects, ensuring performance and scalability.
Design and implement modern data architectures, including Data Lakes, Data Warehouses, and Lakehouse solutions.
Collaborate with business stakeholders, architects, and engineering teams to define data strategies and roadmaps.
Provide technical leadership and mentorship to Data Engineers and Developers.
Ensure best practices around:

Data governance
Data quality
Security and compliance
Performance optimization

Lead data platform modernization initiatives across cloud environments.
Review solution designs, architecture documents, and implementation approaches.
Manage project planning, resource allocation, risks, and delivery timelines.
Drive Agile delivery and ensure successful project execution.

Requirements

8+ years of experience in Data Engineering and Data Platform delivery.
Minimum 3+ years of experience in a Technical Manager or Lead role.
Proven experience in delivering:

Data Lake implementation projects
Data Lake migration programs
PySpark migration projects
Large-scale data transformation initiatives

Technical Skills

Strong expertise in:

Python
PySpark
Spark SQL
SQL
ETL/ELT frameworks

Experience with:

Hadoop ecosystem
Data Lakes and Lakehouse architectures
Distributed data processing frameworks

Strong understanding of:

Data modeling
Data integration patterns
Batch and real-time processing

Experience with cloud platforms such as:

AWS
Azure
GCP

Hands-on experience with:

Data migration strategies
Performance tuning and optimisation
CI/CD and DevOps practices for data platforms

Must Have

1. Hands-on experience in managing Data projects end-to-end — effort estimation, scoping, project plan, timelines, team allocation, stakeholder management

2. Hands-on experience with modern Data technologies like PySpark, SQL, CML, Python on any cloud; preferred GCP

3. Transform business requirements into Data solutions, manage risks, issues and dependencies

Good To Have

Preferred Cloud GCP