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Data Platform & Analytics Lead; Microsoft Fabric​/AI Enablement

Job in Manchester, Greater Manchester, M9, England, UK
Listing for: BCN Group
Full Time position
Listed on 2026-06-12
Job specializations:
  • IT/Tech
    Data Analyst, AI Engineer (Applied/Software), Data Engineering, Data Science Manager
Salary/Wage Range or Industry Benchmark: 80000 - 100000 GBP Yearly GBP 80000.00 100000.00 YEAR
Job Description & How to Apply Below
Position: Data Platform & Analytics Lead (Microsoft Fabric / AI Enablement)

Job title: Data Platform & Analytics Lead (Microsoft Fabric / AI Enablement)

Location: Manchester or Leeds (hybrid working available)

Hours: Monday to Friday, 37.5 hours per week

Salary: Competitive + benefits

About BCN:

At BCN we unite people and technology to enable organisations to fly.

We believe people and organisations can achieve anything using technology to it’s full potential. Our role is to help them understand what is possible, implement in the right way and utilise their technology to achieve their ambitions. Which is why we put people front and centre – building client relationships for life and fostering a culture where our people thrive.

We are a leading managed IT services provider and technology consultant, specialising in delivering transformative technology solutions with industry‑leading client experience across business, public sector and not for profit organisations. From cloud computing, cybersecurity, and data management to power app development, we are dedicated to pioneering technology with Microsoft innovation.

Guided by our 3 values of building relationships, customer success and passion and dedication
, we are on a mission to make BCN the most trusted tech partner in the UK today
. The kind of company clients want to work with, and people want to work for.

We are delighted you are on this journey with us!

Focus of the role

The Data & Analytics Lead owns BCN’s enterprise data platform and is responsible for ensuring it supports analytics, AI, and machine learning use cases in a structured, governed, and scalable way. This includes end-to-end accountability for data ingestion, transformation, lakehouse design, semantic modelling, and the development of reliable data products. The role ensures business data is accessible, consistent, and trusted, with clearly defined metrics that enable high‑quality analysis, automation, and decision‑making.

It also establishes a strong platform foundation that accelerates the adoption of AI and advanced analytics, while maintaining appropriate governance, performance, and control.

Responsibilities
1) Data Platform & Lakehouse Architecture
  • Own the design and operation of BCN’s data platform within Microsoft Fabric.
  • Define and implement lakehouse architecture patterns, including data layering, structure, storage and organisation.
  • Manage ingestion, transformation and storage of data across the platform.
  • Ensure the platform supports both analytical and AI/ML workloads.
2) Data Modelling & Semantic Layer
  • Define and maintain core business metrics and calculations.
  • Ensure consistent definitions across all datasets and outputs.
  • Develop and manage semantic models used by the business.
  • Ensure data structures are suitable for both analytics and machine learning use.
3) Data Products & Analytics
  • Deliver structured datasets and analytical outputs aligned to business requirements.
  • Maintain and enhance existing reporting and analytics solutions.
  • Ensure outputs are aligned to operational processes and clearly understood.
  • Reduce duplication and fragmentation of data across the organisation.
4) AI & Machine Learning Enablement
  • Define how AI and machine learning will operate on BCN’s data platform.
  • Ensure data is structured, accessible and governed to support AI/ML use cases.
  • Identify and develop repeatable patterns for applying AI/ML to business data.
  • Enable integration of AI outputs into existing data products and workflows.
  • Ensure AI/ML outputs are traceable, controlled and aligned with defined metrics.
5) Delivery Ownership & Roadmap
  • Take ownership of the current data platform implementation.
  • Assess current architecture, datasets and capabilities.
  • Maintain and prioritise the existing backlog, incorporating new requirements.
  • Define and maintain a forward‑looking roadmap for data platform and AI capability.
  • Establish a structured delivery approach aligned to business priorities.
6) Governance & Control
  • Implement data ownership, stewardship and access controls.
  • Ensure documentation, version control and change management are maintained.
  • Maintain auditability and traceability of data, calculations and AI outputs.
  • Ensure appropriate control frameworks are applied to AI and machine learning…
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