Data engineers are critical and highly valued. With UK data engineer salaries averaging around £55,000, understanding salary trends ensures hiring managers stay competitive in attracting top data talent.
This guide provides essential salary insights, market benchmarks and practical tips to shape compelling compensation packages for data engineer roles.
What is the Average Data Engineer Salary in the UK?
The average annual salary for a data engineer in the UK is between £55,000 and £70,000 per year, depending on experience and position. We’ve compiled a table demonstrating how this range can vary based on experience, company size and industry.
Category | Salary Range | Average | Median |
Entry-Level (0–2 yrs) | £41,561 – £50,000 | £55,000 | £43,000 |
Mid-Level (2–5 yrs) | £50,000 – £70,000 | £60,000 | £55,000 |
Senior (5+ yrs) | £75,000 – £100,000+ | £85,000 | £75,000 |
Startup (any level) | £35,000 – £65,000 | £50,000 | £45,000 |
Large Enterprises | £60,000 – £90,000 | £75,000 | £70,000 |
Finance / Banking | £70,000 – £100,000+ | £85,000 | £80,000 |
Retail / E-Commerce | £50,000 – £75,000 | £60,000 | £55,000 |
Healthcare / BioTech | £55,000 – £80,000 | £65,000 | £60,000 |
- Entry-level data engineers earn around £41,561 to £50,000, with an average salary of £55,000.
- Mid-level professionals with 2-5 years of experience typically earn between £50,000 and £70,000, averaging £60,000.
- Senior data engineers with 5+ years of experience can command salaries from £75,000 to over £100,000, with a median of £85,000.
- Company size influences salary – startups may offer between £35,000 and £65,000, while large enterprises can offer £60,000 to £90,000.
- Industry also plays a role – finance and banking sectors offer higher salaries, ranging from £70,000 to over £100,000, due to the critical nature of data in these fields.
Regional Differences in Data Engineer Salaries
Data engineer salaries in the UK vary significantly by region, with London commanding the highest average pay. Remote roles also offer competitive compensation, often aligning with or exceeding regional averages.
Region | Average Salary | Salary Range |
|---|---|---|
London | £75,000 – £100,000 | £50,000 – £125,000 |
Birmingham | £53,953 | £45,000 – £65,000 |
Manchester | £52,775 | £47,000 – £70,000 |
Edinburgh | £53,433 | £45,000 – £65,000 |
Leeds | £50,640 | £44,000 – £60,000 |
Newcastle upon Tyne | £53,464 | £45,000 – £60,000 |
Remote (UK-wide) | £65,000 | £54,000 – £85,000 |
- London has the highest data engineer salaries, reflecting its status as a major tech and financial hub. Data engineers here can expect salaries between £75,000 and £100,000, with senior roles reaching up to £125,000.
- Other major cities like Birmingham, Manchester and Edinburgh offer competitive salaries, typically ranging from £45,000 to £70,000, depending on experience and company size.
- Remote roles have become increasingly prevalent, offering flexibility and salaries that often match or exceed regional averages. The average remote data engineer salary in the UK stands at £65,000, ranging between £54,000 and £85,000.
What is the Highest Salary for a Data Engineer in the UK?
The highest salaries for data engineers in the UK are typically found in senior and lead roles within large organisations, particularly in London. According to Morgan McKinley, senior data engineers in London can earn between £90,000 and £125,000 per year.
Those in senior data engineer jobs are responsible for designing and developing large-scale data processing software, leading data engineering initiatives and implementing modern data modelling practices.
They have experience in:
- Building data pipelines on cloud platforms
- Using programming and data engineering tools
- Delivering secure, robust, and scalable data engineering solutions to cross-functional teams.
In addition to competitive salaries, lead data engineer roles often come with perks and benefits such as discretionary annual bonuses, generous annual leave, private medical insurance and more.
These roles are critical in organisations that rely heavily on data for their operations. The demand for skilled data engineers continues to grow across various sectors in the UK.
What Factors Influence Data Engineer Salaries?
Several key factors significantly influence the amounts that data engineers earn in the UK. Understanding these factors can help professionals strategically plan their careers and negotiate better compensation.
- Company Size: Well-established, larger companies generally offer higher salaries due to their scale, resources and complexity of data infrastructure. Early-stage startups might offer lower salaries but supplement compensation with equity stakes, granting potential future rewards.
- Industry Specialisation: Industries with critical data-dependency, such as finance, healthcare, pharmaceuticals and technology, often pay higher salaries.
- Skills and Technical Expertise: Core technical skills substantially influence compensation. Data engineers proficient in cloud platforms (AWS, Azure, GCP), big data frameworks (Hadoop, Spark) and data pipeline tools (Kafka, Airflow, Databricks) command premium salaries.
- Membership of a Professional Body: Candidates affiliated with professional bodies such as the Alliance for Data Science Professionals (AfDSP), while not directly guaranteeing higher pay, enhances employability and adds credibility that signals a commitment to maintaining professional standards.
- Qualifications and Certifications: Formal education, especially relevant postgraduate qualifications, can significantly enhance earning potential. Professional achievements like cloud certifications are also highly valued and frequently lead to salary premiums.
- Knowledge of Specific Tools: Hands-on experience with specific industry-standard software and tools significantly influences salary negotiations. High-demand tools include: Cloud platforms, big data engineering technologies, pipeline and integration tools and data warehousing solutions.
How to Determine a Competitive Data Engineer Salary
Determining a competitive data engineer salary is crucial for attracting top talent without overspending your hiring budget.
Here’s a practical guide with key insights to help hiring managers navigate salary benchmarking effectively:
- Utilise Reliable Market Data: Use authoritative salary guides, like those from Glassdoor, Morgan McKinley, Robert Half and IT Jobs Watch, or use a salary benchmarking tool. These sources provide real-time data on market rates by region, seniority and industry.
- Compare Similar Roles and Industries: Evaluate salaries for similar positions within your industry. Salaries in finance or tech companies tend to be significantly higher compared to retail or public sectors due to demand and data complexity.
- Consider Company Size: Recognise how salary expectations differ between startups, mid-sized firms and large enterprises. Larger companies often have structured salary bands, while startups leverage equity compensation to stay competitive.
- Consider Alternative Compensation: To stay attractive without inflating base pay offers, you can explore complementary compensation methods like performance-based bonuses, equity and stock options or other additional benefits.
- Understanding What Drives Candidates: Focus your budget on aspects candidates value highly, such as career development opportunities, the latest tools or working with cutting-edge technologies.
- Highlight Non-Monetary Benefits: Promote positive company culture, flexible working arrangements, career progression opportunities and meaningful work. These can strongly influence candidate decisions without additional salary expense.
- Tailor Offers to Individual Preferences: Be flexible and tailor your compensation package based on what matters most to individual candidates (e.g., extra holidays versus increased pension contributions).
- Get Support from Experts (Live Digital): Consider partnering with data recruitment experts like Live Digital. We know the data recruitment industry inside out and can advise on crafting competitive, financially sustainable salary and compensation packages that attract and retain top-tier data engineers.
- Utilise Reliable Market Data: Use authoritative salary guides, like those from Glassdoor, Morgan McKinley, Robert Half and IT Jobs Watch, or use a salary benchmarking tool. These sources provide real-time data on market rates by region, seniority and industry.
FAQs about Data Engineer Salaries
Q: Are data engineers highly paid?
Yes, data engineers are generally well compensated. In the UK, the average salary for a Data Engineer is around £55,000 per year. Senior-level roles or positions in industries like finance or technology can push salaries significantly higher, often over £90,000.
Q: What is the average salary of a data engineer with two years of experience?
A data engineer in the UK with about two years of experience typically earns an average salary of approximately £45,000–£55,000 per year.
Q: What companies tend to hire data engineers?
Companies across various sectors hire data engineers, especially those heavily reliant on data-driven decisions.
- Financial services: Barclays, HSBC, J.P. Morgan
- Technology: Amazon, Google, Microsoft
- Retail and E-commerce: Tesco, ASOS, Ocado
- Healthcare and Pharmaceuticals: AstraZeneca, GSK, NHS
- Consulting: Deloitte, Accenture, KPMG
Q: Are data engineers still in demand?
Yes, data engineers remain highly sought after. Companies increasingly recognise data infrastructure as a strategic priority, ensuring sustained demand in the data engineer job market.
What is the typical career path for a data engineer?
- Entry-Level: A junior data engineer builds and maintains data pipelines, working closely under senior engineers or architects.
- Mid-Level: A data engineer or cloud data engineer owns projects and specialises in specific technologies or platforms.
- Senior Data Engineer or Data Architect: These are leadership roles, managing teams, overseeing infrastructure strategy and configuring complex data solutions.
- Head of Data Engineering or Chief Data Officer (CDO): Strategic positions that focus on company-wide data strategy, governance and innovation.
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