Salary for Mid-level / Intermediate Data Engineer in United Kingdom during 2024

Salary details

The average mid-level / intermediate Data Engineer salary lies between USD 51,446 and USD 86,163 in the United Kingdom. It represents the overall compensation/gross salary amount for the working year (before deductions like social security, taxes and other contributions), not including equity/stock options or similar benefits.

Region represents the primary country of residence of an employee during the year (or residence for tax purposes). All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.

Last updated: Feb. 16, 2025

Salary trend

Top 20 Job Tags for Mid-level / Intermediate Data Engineer roles

The three most common job tag items assiciated with mid-level / intermediate Data Engineer job listings are Engineering, SQL and Python. Below you find a list of the 20 most occuring job tags in 2024 and the number of open jobs that where associated with them during that period:

Engineering | 3820 jobs SQL | 3775 jobs Python | 3718 jobs Pipelines | 3274 jobs ETL | 2780 jobs Data pipelines | 2515 jobs AWS | 2332 jobs Computer Science | 2183 jobs Architecture | 1984 jobs Spark | 1880 jobs Azure | 1876 jobs Security | 1721 jobs Big Data | 1610 jobs Agile | 1587 jobs Data quality | 1410 jobs Machine Learning | 1384 jobs Java | 1239 jobs GCP | 1195 jobs Snowflake | 1182 jobs Testing | 1172 jobs

Top 20 Job Perks/Benefits for Mid-level / Intermediate Data Engineer roles

The three most common job benefits and perks assiciated with mid-level / intermediate Data Engineer job listings are Career development, Health care and Flex hours. Below you find a list of the 20 most occuring job perks or benefits in 2024 and the number of open jobs that where offering them during that period:

Career development | 2905 jobs Health care | 1503 jobs Flex hours | 1183 jobs Startup environment | 872 jobs Competitive pay | 866 jobs Team events | 815 jobs Equity / stock options | 712 jobs Flex vacation | 636 jobs Parental leave | 607 jobs Salary bonus | 607 jobs Insurance | 595 jobs Medical leave | 525 jobs Wellness | 415 jobs 401(k) matching | 342 jobs Fitness / gym | 163 jobs Home office stipend | 131 jobs Relocation support | 129 jobs Unlimited paid time off | 125 jobs Gear | 121 jobs Transparency | 117 jobs

Salary Composition for a Mid-level Data Engineer in the UK

The salary for a mid-level data engineer in the UK typically comprises a fixed base salary, performance bonuses, and additional remuneration such as stock options or benefits. The fixed base salary is the most significant component, often accounting for 70-85% of the total compensation package. Performance bonuses can vary widely, ranging from 5-20% of the base salary, depending on the company's performance and individual contributions. Additional remuneration might include stock options, especially in tech startups or larger tech companies, and benefits such as health insurance, pension contributions, and professional development allowances. The composition can vary significantly based on the region, with London-based roles often offering higher salaries due to the cost of living. Industry also plays a role; for instance, finance and tech sectors might offer more competitive packages compared to other industries.

Steps to Increase Salary from a Mid-level Position

To increase your salary from a mid-level data engineer position, consider the following strategies:

• Skill Enhancement: Continuously update your technical skills, especially in emerging technologies like cloud computing, big data frameworks, and machine learning.
• Specialization: Develop expertise in a niche area, such as data architecture or machine learning engineering, which can command higher salaries.
• Networking: Build a strong professional network through industry events, conferences, and online platforms like LinkedIn to learn about higher-paying opportunities.
• Advanced Education: Pursue further education, such as a master's degree in data science or a related field, which can open doors to senior roles.
• Certifications: Obtain relevant certifications that can validate your skills and make you more attractive to employers.
• Performance and Negotiation: Consistently deliver high-quality work and be prepared to negotiate your salary during performance reviews or when offered a new position.

Educational Requirements for a Mid-level Data Engineer

Most mid-level data engineer positions require a bachelor's degree in computer science, information technology, engineering, or a related field. Some employers may prefer candidates with a master's degree, especially for roles that involve complex data systems or advanced analytics. A strong foundation in mathematics and statistics is also beneficial, as these skills are crucial for data analysis and algorithm development.

Helpful Certifications for Data Engineers

Several certifications can enhance your credentials as a data engineer:

• AWS Certified Data Analytics: Validates expertise in using AWS data lakes and analytics services.
• Google Professional Data Engineer: Demonstrates proficiency in designing, building, and operationalizing data processing systems on Google Cloud.
• Microsoft Certified: Azure Data Engineer Associate: Focuses on implementing data solutions using Azure services.
• Cloudera Certified Professional (CCP) Data Engineer: Recognizes advanced skills in data engineering using Cloudera's platform.

These certifications can help you stand out in the job market and may lead to higher salary offers.

Experience Required for Mid-level Data Engineer Roles

Typically, mid-level data engineer roles require 3-5 years of experience in data engineering or related fields. This experience should include hands-on work with data pipelines, ETL processes, and data warehousing solutions. Familiarity with programming languages such as Python, Java, or Scala, and experience with big data technologies like Hadoop, Spark, or Kafka, are often expected. Experience with cloud platforms (AWS, Azure, Google Cloud) is increasingly important as more companies migrate their data infrastructure to the cloud.

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