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

Salary details

The average mid-level / intermediate Analytics Engineer salary lies between USD 87,500 and USD 115,000 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: Nov. 17, 2024

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

The three most common job tag items assiciated with mid-level / intermediate Analytics Engineer job listings are SQL, Engineering 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:

SQL | 425 jobs Engineering | 371 jobs Python | 329 jobs Pipelines | 285 jobs dbt | 244 jobs ETL | 205 jobs Data Analytics | 201 jobs Data pipelines | 187 jobs Tableau | 184 jobs Data quality | 181 jobs Computer Science | 179 jobs Snowflake | 156 jobs Looker | 150 jobs Airflow | 149 jobs Architecture | 138 jobs Business Intelligence | 135 jobs Testing | 130 jobs AWS | 127 jobs Data visualization | 126 jobs BigQuery | 114 jobs

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

The three most common job benefits and perks assiciated with mid-level / intermediate Analytics Engineer job listings are Career development, Health care and Startup environment. 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 | 300 jobs Health care | 203 jobs Startup environment | 148 jobs Flex hours | 140 jobs Equity / stock options | 136 jobs Competitive pay | 128 jobs Team events | 106 jobs Parental leave | 99 jobs Insurance | 97 jobs Flex vacation | 96 jobs Medical leave | 85 jobs Salary bonus | 67 jobs Home office stipend | 61 jobs 401(k) matching | 57 jobs Wellness | 52 jobs Unlimited paid time off | 49 jobs Fitness / gym | 45 jobs Conferences | 41 jobs Gear | 27 jobs Transparency | 23 jobs

Salary Composition

In the United Kingdom, the salary composition for a Mid-level/Intermediate Analytics Engineer can vary based on several factors such as region, industry, and company size. Typically, the salary is divided into three main components:

• Base Salary: This is the fixed annual amount and usually constitutes the largest portion of the total compensation package. For a role in AI/ML/Data Science, the base salary can range significantly depending on the location (e.g., London vs. other regions), with London often offering higher base salaries due to the cost of living and demand for tech talent.

• Bonus: Bonuses are often performance-based and can be tied to individual, team, or company performance. In tech companies or startups, bonuses might also be linked to project milestones or product launches. The bonus can range from 5% to 20% of the base salary, depending on the company's policy and the employee's performance.

• Additional Remuneration: This can include stock options, profit-sharing, or other incentives. Larger tech companies or startups might offer stock options as part of their compensation package, which can be a significant financial benefit if the company performs well. Other benefits might include health insurance, pension contributions, and other perks like gym memberships or travel allowances.

Increasing Salary

To increase your salary from a Mid-level/Intermediate position, consider the following steps:

• Skill Enhancement: Continuously update and expand your skill set. Specializing in high-demand areas such as deep learning, natural language processing, or big data analytics can make you more valuable.

• Advanced Education: Pursuing further education, such as a master's degree or specialized certifications, can enhance your qualifications and open up higher-paying opportunities.

• Networking: Engage with professional networks and communities. Building relationships with industry leaders and peers can lead to new opportunities and insights into higher-paying roles.

• Leadership Roles: Aim for roles that offer leadership responsibilities, such as team lead or project manager positions, which typically come with higher compensation.

• Industry Shift: Consider moving to industries that offer higher salaries for analytics roles, such as finance, healthcare, or technology.

Educational Requirements

Most Mid-level/Intermediate Analytics Engineer positions require at least a bachelor's degree in a relevant field such as Computer Science, Data Science, Statistics, Mathematics, or Engineering. Some employers may prefer candidates with a master's degree, especially for roles that involve complex data analysis or machine learning model development.

Helpful Certifications

While not always mandatory, certain certifications can enhance your profile and demonstrate your expertise:

• Certified Analytics Professional (CAP): This certification is recognized globally and covers a broad range of analytics skills.

• Google Professional Data Engineer: This certification is valuable for those working with Google Cloud Platform and focuses on designing, building, and operationalizing data processing systems.

• AWS Certified Machine Learning – Specialty: Ideal for those working with AWS, this certification validates your ability to build, train, and deploy machine learning models on the AWS platform.

• Microsoft Certified: Azure Data Scientist Associate: This certification is beneficial for professionals working with Azure and focuses on applying data science techniques to implement and run machine learning workloads.

Experience Requirements

Typically, a Mid-level/Intermediate Analytics Engineer is expected to have 3-5 years of relevant experience. This experience should include hands-on work with data analysis, machine learning model development, and data engineering. Experience with programming languages such as Python or R, as well as familiarity with data visualization tools and cloud platforms, is often required.

Related salaries

Want to contribute?