Data Engineer | Hybrid | London | Databricks | Azure | 85k

City of London
3 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Informatica Data Engineer

Lead Data Engineer (Azure/Databricks)

Lead Starburst Data Engineer

Data Architect (DV)

Data Engineer

Data Engineer | Hybrid | London | Databricks | Azure | 85k

I'm working with a global powerhouse that's been setting the standard for excellence for over 60 years. With more than 1,000 projects delivered worldwide and a combined value exceeding $150 billion, they've earned a reputation as a trusted leader in high-value, complex projects. Today, their 2,500-strong team spans three continents, driving innovation and growth at scale.

What truly makes this company stand out is its people-first culture. They champion respect, inclusion, and genuine care for their employees, backed by a flexible hybrid model that gives you control over which three office days you work each week. This is an organisation where world-class projects meet an environment that prioritises your well-being and career development.

I'm looking for a Data Engineer who thrives on innovation and loves tackling complex data challenges. If building scalable, cloud-based solutions excites you, this is your chance to make a real impact. You'll work with cutting-edge technology and stay at the forefront of the data engineering field.

You'll Work With

Azure Data Services: Data Factory, Data Lake, SQL
Databricks: Spark, Delta Lake
Power BI: Advanced dashboards and analytics
ETL & Data Modelling: T-SQL, metadata-driven pipelines
Design and implement scalable Azure-based data solutions
Build and optimise data pipelines for integration and transformation
Develop Power BI dashboards for global stakeholders
Ensure data quality, governance, and security
Collaborate in an Agile environment with cross-functional teamsBenefits

Competitive salary up to £85k + 10% discretionary bonus
8% non-contributory pension, private medical insurance, virtual GP access
25 days annual leave (option to buy more), volunteering day, extra leave with tenure
Lifestyle perks: dental plans, season ticket loans, discounted gym memberships, cycle-to-work scheme
A high-performance, high-trust environment with global exposure and flexibilityKey experience

Hands-on experience with Azure & Databricks
Strong data engineering and modelling skills
Proficiency in Power BI, T-SQL, DAX
Ability to troubleshoot complex data issues and deliver solutions

Interviews are happening now don't wait to take the next step in your career. Apply today and secure your opportunity to join a leading team

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 Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.