Technical Data Engineer

Northampton
6 months ago
Applications closed

Related Jobs

View all jobs

Technical Data Engineer / Analyst

Senior Data Engineering Consultant

Data Engineer - (Python, SQL, Machine Learning) - Robotics

Data Engineer

Data Engineer

Data Engineer

Technical Data Engineer

Location: Northampton or London / Hybrid 2-3 days on-site

6 Months +

We are seeking a highly skilled and communicative Technical Data Engineer to join our team. The ideal candidate will have hands-on experience with modern data platforms and tools including Databricks, DBT, and Snowflake. You will play a key role in designing, developing, and optimizing data pipelines and analytics solutions that drive business insights and decision-making.

Key Responsibilities:

Design, build, and maintain scalable data pipelines using Databricks and DBT.
Develop and optimize data models and transformations in Snowflake.
Collaborate with cross-functional teams to understand data requirements and deliver robust solutions.
Ensure data quality, integrity, and governance across platforms.
Troubleshoot and resolve data-related issues in a timely manner.
Document processes, workflows, and technical specifications clearly and effectively.Required Skills & Experience:

Proven hands-on experience with:
Databricks (Spark, Delta Lake, notebooks)
DBT (data modeling, transformations, testing)
Snowflake (SQL, performance tuning, data warehousing)
Strong understanding of data engineering principles and best practices.
Excellent communication skills with the ability to explain technical concepts to non-technical stakeholders.
Experience working in agile environments and collaborating with data analysts, scientists, and business teams.

All profiles will be reviewed against the required skills and experience. Due to the high number of applications we will only be able to respond to successful applicants in the first instance. We thank you for your interest and the time taken to apply

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.