Data Engineer

Experienced Data Engineer

We’re looking for an experienced, proactive Data Engineer to join UCS College Group as part of our Digital Development team.

This is a mid-level role suited to someone with strong practical SQL experience who enjoys working directly with data: extracting it, cleaning it, modelling it, validating it, and turning it into reliable datasets that the organisation can trust.

The immediate focus of the role is to strengthen how we structure, manage, and use data across the organisation. This is a hands-on role working directly with source systems, staging tables, SQL transformations, data quality rules, and curated semantic data models that support reporting, analytics, statutory returns, and operational decision-making.

You’ll work closely with reporting teams, software engineers, DevOps colleagues, and business stakeholders to understand analytical requirements and translate them into clean, governed, reusable data models. Your work will help ensure that data is accurate, consistent, timely, and genuinely useful across the College Group.

In addition to day-to-day delivery, you’ll help shape our broader Data Strategy and Digital Roadmap as we transition towards a lakehouse-supported medallion architecture, with a particular focus on developing trusted silver and gold-layer datasets.

You’ll work within a hybrid-cloud Azure environment, contributing to scalable data solutions that improve data quality, reduce manual work, and support evidence-based decision-making across the organisation.

This is not primarily a Power BI dashboard development role. Power BI experience is useful, but the core requirement is the ability to build robust, well-modelled, SQL-driven data layers that reporting and analytics tools can consume confidently.

What You’ll Be Doing

You’ll take responsibility for the design, development, optimisation, and support of SQL-based data processes and curated analytical data models. This includes:

• Designing, building, and maintaining robust ETL/ELT processes using SQL and Microsoft data technologies
• Extracting data from internal and external source systems and transforming it into reliable, structured datasets
• Building and maintaining staging layers, holding tables, transformed silver-layer datasets, and curated gold-layer semantic models
• Writing, reviewing, and optimising SQL queries, stored procedures, views, and transformations
• Creating reporting-ready data structures that are accurate, reusable, and easy for reporting teams to consume
• Applying data quality rules, validation checks, deduplication logic, and reconciliation processes
• Modelling data to support real organisational questions around academic performance, operations, compliance, and strategic planning
• Designing fact and dimension-style structures where appropriate to support analytical reporting
• Improving performance, reliability, and maintainability of existing SQL processes
• Monitoring data flows, diagnosing issues, and resolving data pipeline or data quality problems
• Working with stakeholders to understand definitions, business rules, and reporting requirements
• Supporting datasets and reports by ensuring the underlying data models are clean, trusted, and fit for purpose
• Applying secure data practices, including role-based access controls and appropriate handling of sensitive information
• Contributing to documentation, data definitions, standards, and engineering best practices