Data & Analytics Lead ​/ Architect

Data & Analytics Lead / Architect

PBT Group is looking for an experienced Data & Analytics Lead / Architect to join our consulting team and play a key role in shaping and delivering modern Data & Analytics solutions for our clients.

This is a senior, hands-on leadership role suited to an individual who can operate across Data Architecture, Data Engineering, Data Science and Application Architecture
, while providing technical direction and leadership across multiple teams and stakeholders.

The successful candidate will be responsible for defining and driving Data & Analytics architecture, strategy and delivery
, ensuring that data platforms and solutions are scalable, secure, governed and aligned to business objectives.

The role will involve working across multiple technical disciplines, providing architectural direction to Data Engineering, Data Science, BI and broader IT teams
, while also engaging with senior business and technology stakeholders.

The ideal candidate will be comfortable moving between strategic architecture and hands-on technical problem solving
, with a strong understanding of modern cloud and data platforms.

• Define and drive Data & Analytics strategies, architectures and implementation roadmaps
  .
• Provide technical leadership across Data Engineering, Data Science, BI and supporting IT teams
  .
• Lead and mentor technical resources, promoting engineering and architecture best practices.
• Design and govern modern data platforms, data warehouses, data lakes and analytics environments
  .
• Provide architectural guidance across cloud and hybrid-cloud environments
  , with particular exposure to Azure and AWS.
• Lead the adoption and implementation of Microsoft Fabric and other modern Data & Analytics technologies.
• Provide technical direction on data ingestion, integration, transformation, orchestration and data pipelines.
• Define and maintain data modelling, data architecture, data governance and data quality standards
  .
• Collaborate with Application Architects and development teams to ensure appropriate integration between application and data architectures
  .
• Evaluate existing technology environments and recommend appropriate modernisation and migration strategies.
• Provide architectural input into cloud migration and hybrid on-premise/cloud solutions
  .
• Work closely with Data Scientists to enable scalable data and AI/ML solutions.
• Support the adoption of Generative AI and AI-enabled Data & Analytics solutions
  , identifying practical opportunities for business application.
• Assess emerging technologies and provide recommendations on their applicability within client environments.
• Drive proof-of-concepts and technical evaluations where required.
  
  Establish and improve technical ways of working, development standards and architectural governance.
• Participate in technical pre-sales, solution estimation and client engagements where required.
• Communicate complex technical concepts effectively to both technical and non-technical stakeholders.

The successful candidate should have strong experience across a broad modern Data & Analytics landscape, including:

• Data Architecture
• Data Modelling
• Data Warehousing
• Data Lakes / Lakehouse architectures
• Data Integration & ETL/ELT
• Data Pipelines and orchestration
• Data Governance and Data Quality
• Metadata and Data Lineage
• Modern Data Platform architecture

• Microsoft Fabric
• Azure Data Factory
• Azure Data Lake
• Azure Synapse
• SQL Server / Azure SQL
• Power BI
• Microsoft Purview or equivalent governance technologies

• Strong Azure experience
• Exposure to AWS and hybrid-cloud architectures
• Understanding of cloud migration and modernisation strategies
• Experience integrating on-premise and cloud data environments

• Strong SQL
• Python and/or another modern programming language
• Data Engineering frameworks and development practices
• CI/CD and Dev Ops principles
• API and application integration
• Understanding of microservices and modern application architectures

• Understanding of Data Science environments and requirements
• Machine Learning concepts and data pipelines
• Gene…