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

London
1 week ago
Create job alert

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

Related Jobs

View all jobs

Senior Data Analyst - Trading Data Specialist EMEA (F/M/D)

Health data specialist

Assessor / Trainer - Data Technician and Business Analyst

Assessor / Trainer - Data Technician and Business Analyst

Assessor / Trainer - Data Technician and Business Analyst

Senior Analyst & Data Specialist

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.