Senior Data Engineer

London
3 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer - (ML and AI Platform Engineering)
Location: London hybrid working Monday to Wednesday in the office
Salary: £70,000 to £80,000 depending on experience
Ref: J13026

We are working with an AI first SaaS business that transforms complex first party data into trusted, decision ready insight at scale.
The company is scaling thoughtfully and investing heavily in its data and AI platform. You will join an engineering team where quality, ownership, and collaboration matter, and where you will be heard, supported, and given real space to grow. This role will suit someone who enjoys building production grade data and ML pipelines, and who takes pride in strong modelling, transformation, and engineering standards.

What you will be doing
• Building, shaping, and owning cloud native data and machine learning pipelines end to end
• Designing and implementing robust data transformations and modelling logic using Python, PySpark and SQL
• Developing scalable data structures that support analytics, ML, and downstream product use cases
• Strengthening CI CD pipelines, deployments, monitoring, and overall platform reliability
• Partnering closely with product, engineering, and data science teams to deliver production outcomes rather than experiments
• Helping define engineering standards, reusable patterns, and ways of working as the data and AI platform evolves

What you will bring
• Strong hands on experience with Python and PySpark, particularly for complex data modelling and transformation
• Strong SQL skills with experience building and optimising analytical and production ready data models
• Proven experience delivering production data platforms and pipelines at scale, not just prototypes
• Experience working with cloud based data platforms and modern engineering practices including CI CD, observability, and automated testing
• A collaborative mindset where you actively share knowledge, support others, and raise engineering standards
• Strong communication skills with the ability to work effectively with both technical and non-technical stakeholders

Why this team
• A supportive and inclusive culture where every voice is heard and respected
• Leadership that genuinely cares about representation and creating space for diverse careers in data and engineering
• Clear progression pathways with options to grow into senior engineering, platform leadership, or deeper AI focused roles
• Strong mentoring, knowledge sharing, and a thoughtful approach to performance, wellbeing, and work life balance

Additional information
Right to work in the United Kingdom is required. Sponsorship is not available.

Apply to learn more or message for a confidential conversation.

If you have a friend or colleague who may be interested, referrals are welcome. For each successful placement, you will be eligible for our general gift or voucher scheme.
Datatech is one of the UK's leading recruitment agencies specialising in analytics and is the host of the critically acclaimed Women in Data event. For more information, visit (url removed)

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.