Senior Data Engineer

London
1 month ago
Create job alert

Company Overview

We are working with an innovative organisation that recognises the increasing complexity of project delivery. Since 2013, our client has been helping companies of all sizes improve the way projects are delivered.

Their mission is to become the number one provider of innovative project solutions, driven by a community of experienced, caring, and passionate professionals who are committed to improving project delivery.

Why Join Our Client?

Our client is currently in an exciting phase of growth, making this an excellent time to join their journey.

They are building something special-scaling the business while maintaining a strong people-first approach. Investment in their teams is a key priority, creating an environment where development is encouraged and individuals are supported to grow with the organisation.

Their culture sets them apart from other consulting practices, and they are looking to build a team that is equally ambitious.

Position Overview

Our client is seeking a Senior Data Engineer who thrives on building scalable, cloud-first data systems.

In this role, you will design and manage data pipelines that support analytics, AI, and automation across complex infrastructure programmes. Your work will play a key part in enabling data-driven transformation across critical UK industries.

Core Responsibilities

Design, build, and optimise data pipelines using Azure Data Factory, Synapse, and Databricks
Develop and maintain ETL/ELT workflows to ensure high data quality and reliability
Collaborate with analysts and AI engineers to deliver robust and reusable data products
Manage data lakes and warehouses using formats such as Delta Lake and Parquet
Implement best practices for data governance, performance, and security
Continuously evaluate and adopt new technologies to evolve the organisation's data platform
Provide technical guidance to junior engineers and contribute to team capability building

Technical Stack

Core:

Azure Data Factory
Azure Synapse Analytics
Azure Data Lake Storage Gen2
SQL Server
Databricks

Enhancements:

Python (PySpark, Pandas)
CI/CD (Azure DevOps)
Infrastructure as Code (Terraform, Bicep)
REST APIs
GitHub
ActionsDesirable:

Microsoft Fabric
Delta Live Tables
Power BI dataset automation
DataOps practices

What You'll Bring

Professional experience in data engineering or cloud data development
Strong understanding of data architecture, APIs, and modern data pipeline design
Hands-on experience within Microsoft's Azure ecosystem, with an interest in emerging technologies such as Fabric, AI-enhanced ETL, and real-time data streaming
Proven ability to lead technical workstreams and mentor junior team members
A strong alignment with the organisation's IDEAL values: Integrity, Drive, Empathy, Adaptability, and Loyalty

Ready to Apply?

This is a fantastic opportunity to join a forward-thinking organisation at a key stage of growth, working on impactful projects across critical industries.

If you're looking to take the next step in your career within a collaborative and innovative environment, we'd love to hear from you

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

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.

New Data Engineering Employers to Watch in 2026: UK and Global Companies Driving the Data Revolution

Data engineering is at the heart of the digital economy, transforming raw data into actionable insights, powering analytics, AI systems, and cloud infrastructure. As the UK and global markets continue to invest heavily in data platforms, pipelines, and real-time analytics, demand for skilled data engineers is growing rapidly. For professionals exploring opportunities on www.DataEngineeringJobs.co.uk , the critical question is: which companies are expanding, hiring, and shaping the future of data-driven business? This article highlights new data engineering employers to watch in 2026, including UK startups, scale-ups, and international firms expanding in the UK.

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.