Building the foundations data depends on.
Every dashboard, model and AI feature depends on data that arrives on time, in the right shape and ready to trust. Data engineers build the pipelines and platforms that make that possible. As UK organisations invest in analytics, machine learning and AI, experienced data engineers are central to turning ambition into something that works.
From start-ups building their first data stack to scale-ups strengthening pipelines after product-market fit and fintechs managing data in regulated environments, we connect data engineers with teams where their work underpins every decision.
Latest roles
City of London, United Kingdom
£450
Posted 23 hours ago
Grimsby, United Kingdom
£60,000 - £60,000
Posted 6 days ago
London, United Kingdom
£70,000 - £75,000
Posted 1 month ago
London, United Kingdom
£70,000 - £85,000
Posted 2 months ago
Starting your career in Data Engineering
There’s no single route into data engineering. A degree in computer science, engineering, maths or a related subject is a common starting point, but it isn’t essential. Many data engineers move across from neighbouring roles:
- Data analysis: building on strong SQL and an understanding of how data is used
- Software engineering: bringing version control, testing and deployment experience
- DevOps or infrastructure: applying automation and cloud skills to data platforms
- Apprenticeships and graduate schemes: combining paid work with structured learning
Whatever your route, Python, SQL and hands-on experience with a cloud platform form the foundation. A portfolio makes a real difference too. A small pipeline project that ingests real data, transforms it and stores it somewhere queryable shows employers you can take something from start to finish. Open source contributions, even small fixes to data tools, show you can work with production-quality code.
Data engineers build and maintain the systems that move, transform and store data, so analysts, data scientists and AI teams can use it with confidence. Day to day, that can include:
- Building pipelines: collecting data from different sources and moving it into warehouses or lakes
- Transforming data: cleaning and structuring it so it’s accurate and easy to use
- Working with cloud platforms: using tools such as Azure, AWS, Databricks or Snowflake
- Monitoring and fixing issues: making sure pipelines run reliably and resolving problems quickly
- Supporting other teams: working with analysts and data scientists to understand what data they need
Early in your career, you’ll often work on parts of existing pipelines while learning how a whole data platform fits together.
SQL and Python are the foundation of most data engineering roles. Alongside them, these skills matter most early on:
- SQL: querying, joining and transforming data reliably
- Python: writing scripts and pipelines to move and process data
- Cloud platforms: a working knowledge of Azure, AWS or Google Cloud
- ETL and ELT: understanding how data is extracted, transformed and loaded, including tools such as dbt
- Data modelling: structuring data so it’s easy to query and understand
- Data quality and testing: checking data is accurate before others rely on it
- Version control and CI/CD: using Git and automated deployment practices
- Data protection awareness: understanding UK GDPR and why access control and lineage matter
As your career develops, tools such as Spark and Databricks, infrastructure as code and system design become increasingly important. Early on, being able to explain how you built and fixed a pipeline often makes a stronger impression than a long list of tools.
Data engineering is among the better-rewarded areas of the UK data market, and pay grows with experience and specialism. Early-career roles build the foundations. As engineers take on cloud platform design, large-scale or real-time processing, automation and technical leadership, earning potential increases. Experience in regulated environments such as financial services, and security clearance for public sector roles, can also strengthen your position over time.
| Seniority Level | Typical Permanent Salary |
| Junior Data Engineer (0-2 years) | £32,000 - £45,000 |
| Data Engineer / Mid-level (2-5 years) | £45,000 - £65,000 |
| Senior Data Engineer | £65,000 - £90,000 |
| Lead / Principal Data Engineer |
£85,000 - £110,000 |
| Data Architect |
£95,000 - £130,000 |
| Head of Data Engineering |
£125,000 - £160,000+ |
Contract data engineering day rates typically sit between £350 and £700+, with a market median close to £500. Specialist Databricks, streaming-pipeline or cloud-migration contracts in London can push past £850, and day rate depends heavily on cloud platform depth, seniority and IR35 status.
London offers the highest advertised data engineering salaries, but the gap with the rest of the UK is relatively modest, and strong opportunities exist across the North, the Midlands and beyond. Home-based and hybrid roles are also available, and generally pay in line with the wider market, so where you live needn’t limit your options. Some public sector roles also require security clearance, which can open a distinct part of the market.
| Seniority Level | Average Permanent Salary |
| London | £60,000 - £135,000 |
| South-East | £55,000- £110,000 |
| Manchester & North-West | £45,000- £95,000 |
| Leeds, Birmingham & regional cities |
£42,000- £90,000 |
| Remote (UK-based) | £48,000- £100,000 |
Advancing your data engineering career
Experienced data engineers can choose between staying deep on the technical side, shaping platforms and architecture, or leading teams.
- Lead Data Engineer: owning the shape of a data platform, setting standards and guiding other engineers
- Principal Data Engineer: solving the hardest technical problems across teams while staying hands-on
- Data Platform Engineer: building the shared infrastructure, tooling and automation other data teams rely on
- Data or Platform Architecture: designing how data platforms, standards and governance fit together across an organisation
- Head of Data Engineering: leading teams and owning the data platform roadmap
The step into senior roles usually means designing for failure, cost and scale, and explaining architectural decisions clearly to the people who will build on them. Mentoring others is also valuable, both for the people you support and as evidence of the leadership many senior roles call for. We can help you understand where your experience is most valued.
The evidence suggests AI is changing data engineering rather than replacing it. In our 2026 Big Tech Employment Survey, the Data & Analytics specialism, which includes data engineering, reported the highest AI-driven efficiency gains of any specialism, with 82% of respondents saying AI has made them more efficient. Rather than reducing demand for data engineers, AI is changing where their time goes.
In practice, AI tools are taking on more of the repetitive work, such as boilerplate pipeline code, first-draft documentation and routine data-cleaning scripts. That frees experienced engineers to focus on platform design, data quality and reliability. AI is also creating new work, as organisations need engineers who can build and run the pipelines and infrastructure that keep AI features and machine learning models reliable in production.
Our view is that AI is making strong data foundations increasingly important. Experienced engineers who can build reliable data platforms for AI and machine learning are well placed for the roles emerging around it.
Data engineering demand spans almost every kind of organisation, so experienced engineers can choose very different environments:
- Start-ups building from 0-1: choosing the first tools and building a data platform from scratch
- Scale-ups after product-market fit: strengthening pipelines, cost control and real-time processing as data volumes grow
- Banks, fintechs, insurers and investment firms: core data platforms, payments data and real-time risk and fraud pipelines, where accuracy and lineage are essential
- Public sector and third sector: modernising long-standing data infrastructure with strong governance and data protection
- Consultancies: delivering data platforms for clients across sectors, in both permanent and contract roles
Many of the most rewarding roles sit in one of two places: established organisations moving from legacy ETL to modern cloud platforms, or growth-stage businesses building a data platform from the ground up with high engineering standards. We can help you understand how teams differ in data maturity, pace and scope.
Yes. Advertised data engineering roles have picked up significantly over the past year, as organisations invest in cloud platforms, analytics and AI. Demand is particularly focused on experienced engineers, with many advertised roles at senior, lead and principal level.
Engineers who combine strong SQL and Python with cloud platform depth, data modelling, automation and data quality expertise are particularly well placed, as are those who understand how data supports AI and machine learning. Security clearance can also open a distinct part of the market in public sector and defence work.
Both platforms offer strong careers, and many organisations use one, the other or both. Databricks is often chosen for large-scale processing, streaming and machine learning workloads, built around Spark. Snowflake is widely used as a cloud data warehouse for analytics and reporting, with a strong focus on simplicity and SQL. Platforms such as BigQuery and Azure’s data services also feature heavily, particularly in organisations committed to Google Cloud or Microsoft.
For experienced engineers, depth in one platform combined with a solid understanding of the underlying concepts, such as data modelling, distributed processing, orchestration and cost management, keeps the widest range of roles open. Those concepts transfer between platforms, even when the tools change.
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