Analytics Engineer: End-to-End Data Pipelines & Power BI

A fast-growing data and AI consultancy is looking for a mid-level Analytics Engineer to join its team and work on modern data platforms, analytics solutions and AI-powered products for enterprise clients.

The business is building the data foundations that enable organisations to get more value from their data - combining modern data engineering, analytics and AI to create trusted, decision-ready information.

This is an opportunity to join at an exciting stage of growth, with the chance to have genuine ownership over the solutions you build and work closely with both technical teams and senior business stakeholders.

The Role

We're looking for an Analytics Engineer who can own the journey from raw data through to meaningful insights.

You'll work across ingestion, transformation, data modelling and the semantic layer, through to Power BI dashboards and visualisations that are actually used by the business.

A big part of the role will be understanding why the business needs the data, not simply building what you're given. You'll work directly with stakeholders to understand their use cases, the decisions they're trying to make, and the metrics and dimensions that matter - then translate those requirements into well-designed, reusable data models.

You'll be equally comfortable in a requirements workshop as you are working in SQL and the data warehouse.

What you'll be doing

• Design and build end-to-end data pipelines, from ingestion and transformation through to modelling and reporting
• Design and maintain semantic layers and dimensional data models that support trusted, self-service analytics
• Build and maintain Power BI dashboards, datasets and visualisations
• Run requirements and discovery sessions with business stakeholders to understand use cases, metrics and dimensions
• Translate business requirements into scalable and well-structured data models
• Establish consistent naming conventions, metric definitions and business logic across analytical models
• Work with modern data engineering tools across ingestion, ELT/ETL, orchestration and transformation
• Use SQL and transformation frameworks such as dbt to develop reliable analytical datasets
• Collaborate with data engineers, data scientists, delivery teams and client stakeholders
• Apply engineering best practice including version control, testing, CI/CD and documentation
• Contribute reusable data modelling patterns and frameworks to improve how future projects are delivered