Senior Data Business Analyst

South Bank
8 months ago
Applications closed

Related Jobs

View all jobs

Data Governance Analyst, Data Owner, Data Business Analyst,City London

Senior Data Governance Analyst

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Senior Data Business Analyst

2-3 days onsite in central London. 

Start Aug - until Jan 2026

Certain Advantage are recruiting on behalf of our globally renowned energies client for a Senior Tech/Data products Business Analyst to work on an important programme delivering a business wide performance dashboard that utilises data from multiple platforms to provide actionable insight for optimising operations.
This is a key role bridging business needs with technical implementation, ensuring that the dashboard delivers actionable insights for optimizing vessel operations across the shipping fleets. Any industry experience is considered but preference for similar projects experience within either maritime, logistics, or energy sectors (if so make sure this is in your CV!).

Skills/Experience

You’ll be required to hit the ground running in understanding the requirements, quickly learn a number of platform data models, and build queries to prove the business requirements before engineering starts building the data products.

Suitable candidates will offer experience in handling big data warehouse projects in an enterprise environment where stakeholders are across multiple countries/timezones.

You’ll have a strong understanding of data modeling and system integration across platforms (even better if this relates to RADAR, GMAS, and BOSS)

Strong proficiency in SQL or similar query languages for building data product designs (to hand to engineers).

Typical responsibilities;

Requirements Gathering: Collaborate with stakeholders to collect and document detailed business requirements for performance metrics.
Comparative Analysis: Analyze and benchmark requirements against the existing business implementation to identify gaps and opportunities for enhancement.
Data Mapping: Map and align data models across multiple systems including RADAR, GMAS, and BOSS to ensure data consistency and integrity.
Data Product Development: Design and build cross-system queries to generate data products that meet business needs.
Stakeholder Engagement: Act as a liaison between business users, data engineers, and developers to ensure alignment and clarity throughout the project lifecycle.
Quality Assurance: Validate data outputs and ensure that the dashboard meets performance, accuracy, and usability standards. 
Does this sound like your next career move? Apply today!
 
Working with Certain Advantage
 
We go the extra mile to find the best people for the job. If you’re hunting for a role where you can make an impact and grow your career, we’ll work with you to find it.
 
We work with businesses across the UK to find the best people in Finance, Marketing, IT and Engineering.
 
If this job isn’t for you, head to (url removed) and register for job alerts and career guidance tips

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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.