BI Specialist (SQL / Azure) - Perm (FTC) - Hybrid

London
9 months ago
Applications closed

Related Jobs

View all jobs

Housing Data Engineer

Lead Data Engineer

Data Analyst Training Course (Excel, SQL & Power BI)

Data Engineer

SC Cleared Data Engineer

Lead Data Engineering Consultant CGEMJP00330718

Role - BI Specialist (SQL / Azure)

Industry - Automotive

Type - Fixed term contract (3 months, extension thereafter)

Rate - £75,000 per annum, pro rata

Location - Hybrid, 50% of the month in the office (London, Victoria)

PURPOSE OF POST:

Experienced Microsoft / Azure Business Intelligence (BI) Specialist to design, build, and support BI solutions across the Microsoft stack, including SSAS, SSRS, and Power BI. The post holder will play a key role in delivering high-quality, enterprise-grade analytics for platforms, while also enabling integration with third-party reporting tools such as Tableau and Amazon QuickSight. The successful candidate will have strong proficiency in SQL and DAX, a solid understanding of Azure data architecture, and experience working in a cross-functional team comprising engineers, analysts, and product stakeholders.

QUALIFICATIONS / SKILLS / ATTRIBUTES

Microsoft BI Stack

Strong hands-on experience with SSAS (both multidimensional and tabular model development)
Experience developing and maintaining SSRS data models and paginated reports
Expertise with Power BI, including Power Query, DAX, measures, and visual designAzure Data Platform

Familiarity with Azure SQL DB, Synapse Analytics, Data Factory, and Azure Analysis Services
Experience managing data refresh strategies, gateways, and Power BI service deployments
Ability to design secure reporting environments with row-level security, role-based access, and Azure AD integrationIntegration & Interoperability

Experience connecting Microsoft BI tools with Tableau, Amazon QuickSight, or similar platforms
Understanding of REST APIs, Power BI Embedded, and programmatic data access patternsData Engineering & Modelling

Strong T-SQL skills for data retrieval and performance tuning
Knowledge of dimensional modelling, star/snowflake schemas, and data warehouse best practices Preferred Qualifications

Microsoft certifications such as DA-100, DP-500, or MCSE: BI
Familiarity with CI/CD for BI assets (e.g. Git integration for SSAS/Power BI)
Exposure to DevOps pipelines for automated deployments
Awareness of data cataloguing, data lineage, and governance standards

MAIN DUTIES INCLUDE:

BI Development & Reporting

Design, develop, and maintain SSAS cubes (tabular and multidimensional) aligned to reporting requirements
Build SSRS data models and reports, ensuring scalability and performance
Develop interactive Power BI dashboards using complex business logic in DAXIntegration & Interoperability

Enable interoperability with third-party tools like Tableau and Amazon QuickSight
Manage secure integrations between Power BI and Azure-hosted data sourcesPlatform Support & Governance

Configure row-level security, user access roles, and workspace settings
Monitor performance across data models and reports; implement best practices for query optimisation
Contribute to the creation of documentation, data standards, and governance artefactsCollaboration & Continuous Improvement

Work closely with data engineers and analysts to define and evolve reporting architecture
Support continuous delivery of BI assets via automated pipelines and DevOps tooling
Drive improvements in data quality, usability, and user self-serviceGCS is acting as an Employment Agency in relation to this vacancy

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

What Hiring Managers Look for First in Data Engineering Job Applications (UK Guide)

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

The Skills Gap in Data Engineering Jobs: What Universities Aren’t Teaching

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.